Bad Seeds: Evaluating Lexical Methods for Bias Measurement

Paper

This repository contains the seed lexicons gathered and evaluated in the below research paper.

Bad Seeds: Evaluating Lexical Methods for Bias Measurement
Maria Antoniak and David Mimno
ACL 2021

Note: When using these seeds, make sure to cite the original papers and give credit to the authors who curated the seed sets. Paper titles and authors are given for each set in the JSON file, and we've included the bibtex for each paper in gathered_seeds.bib.

Data

You can view all the gathered seeds by clicking on the gathered_seeds.json file above. Or you can scroll down to see the seeds in plain markdown.

You can load the seeds into a Pandas dataframe using the following Python snippet:

import pandas as pd
seeds_df = pd.read_json(json_path, orient='records')

If the seed list is empty, this means that we were unable to find a documented seed list for that category and paper.

If you'd like to add new seed sets or improve the documentation for this project, please submit a pull request or contact Maria Antoniak.

Seed Sets

Seeds ID: pleasant-Caliskan_et_al_2017
Category: pleasant
Used in Paper: Semantics derived automatically from language corpora contain human-like biases (Caliskan et al., 2017)
Source Categories: borrowed-from-social-sciences
Link: https://dataverse.harvard.edu/dataset.xhtml?persistentId=doi:10.7910/DVN/DX4VWP
Seeds: [caress, freedom, health, love, peace, cheer, friend, heaven, loyal, pleasure, diamond, gentle, honest, lucky, rainbow, diploma, gift, honor, miracle, sunrise, family, happy, laughter, paradise, vacation]

Seeds ID: unpleasant-Caliskan_et_al_2017
Category: unpleasant
Used in Paper: Semantics derived automatically from language corpora contain human-like biases (Caliskan et al., 2017)
Source Categories: borrowed-from-social-sciences
Link: https://dataverse.harvard.edu/dataset.xhtml?persistentId=doi:10.7910/DVN/DX4VWP
Seeds: [abuse, crash, filth, murder, sickness, accident, death, grief, poison, stink, assault, disaster, hatred, pollute, tragedy, divorce, jail, poverty, ugly, cancer, kill, rotten, vomit, agony, prison]

Seeds ID: flowers-Caliskan_et_al_2017
Category: flowers
Used in Paper: Semantics derived automatically from language corpora contain human-like biases (Caliskan et al., 2017)
Source Categories: borrowed-from-social-sciences
Link: https://dataverse.harvard.edu/dataset.xhtml?persistentId=doi:10.7910/DVN/DX4VWP
Seeds: [aster, clover, hyacinth, marigold, poppy, azalea, crocus, iris, orchid, rose, bluebell, daffodil, lilac, pansy, tulip, buttercup, daisy, lily, peony, violet, carnation, gladiola, magnolia, petunia, zinnia]

Seeds ID: insects-Caliskan_et_al_2017
Category: insects
Used in Paper: Semantics derived automatically from language corpora contain human-like biases (Caliskan et al., 2017)
Source Categories: borrowed-from-social-sciences
Link: https://dataverse.harvard.edu/dataset.xhtml?persistentId=doi:10.7910/DVN/DX4VWP
Seeds: [ant, caterpillar, flea, locust, spider, bedbug, centipede, fly, maggot, tarantula, bee, cockroach, gnat, mosquito, termite, beetle, cricket, hornet, moth, wasp, blackfly, dragonfly, horsefly, roach, weevil]

Seeds ID: instruments-Caliskan_et_al_2017
Category: instruments
Used in Paper: Semantics derived automatically from language corpora contain human-like biases (Caliskan et al., 2017)
Source Categories: borrowed-from-social-sciences
Link: https://dataverse.harvard.edu/dataset.xhtml?persistentId=doi:10.7910/DVN/DX4VWP
Seeds: [bagpipe, cello, guitar, lute, trombone, banjo, clarinet, harmonica, mandolin, trumpet, bassoon, drum, harp, oboe, tuba, bell, fiddle, harpsichord, piano, viola, bongo, flute, horn, saxophone, violin]

Seeds ID: weapons-Caliskan_et_al_2017
Category: weapons
Used in Paper: Semantics derived automatically from language corpora contain human-like biases (Caliskan et al., 2017)
Source Categories: borrowed-from-social-sciences
Link: https://dataverse.harvard.edu/dataset.xhtml?persistentId=doi:10.7910/DVN/DX4VWP
Seeds: [arrow, club, gun, missile, spear, axe, dagger, harpoon, pistol, sword, blade, dynamite, hatchet, rifle, tank, bomb, firearm, knife, shotgun, teargas, cannon, grenade, mace, slingshot, whip]

Seeds ID: european_american_names-Caliskan_et_al_2017
Category: european american names
Used in Paper: Semantics derived automatically from language corpora contain human-like biases (Caliskan et al., 2017)
Source Categories: borrowed-from-social-sciences
Link: https://dataverse.harvard.edu/dataset.xhtml?persistentId=doi:10.7910/DVN/DX4VWP
Seeds: [Adam, Harry, Josh, Roger, Alan, Frank, Justin, Ryan, Andrew, Jack, Matthew, Stephen, Brad, Greg, Paul, Jonathan, Peter, Amanda, Courtney, Heather, Melanie, Katie, Betsy, Kristin, Nancy, Stephanie, Ellen, Lauren, Colleen, Emily, Megan, Rachel]

Seeds ID: african_american_names-Caliskan_et_al_2017
Category: african american names
Used in Paper: Semantics derived automatically from language corpora contain human-like biases (Caliskan et al., 2017)
Source Categories: borrowed-from-social-sciences
Link: https://dataverse.harvard.edu/dataset.xhtml?persistentId=doi:10.7910/DVN/DX4VWP
Seeds: [Alonzo, Jamel, Theo, Alphonse, Jerome, Leroy, Torrance, Darnell, Lamar, Lionel, Tyree, Deion, Lamont, Malik, Terrence, Tyrone, Lavon, Marcellus, Wardell, Nichelle, Shereen, Ebony, Latisha, Shaniqua, Jasmine, Tanisha, Tia, Lakisha, Latoya, Yolanda, Malika, Yvette]

Seeds ID: european_american_names_market_discrimination-Caliskan_et_al_2017
Category: european american names market discrimination
Used in Paper: Semantics derived automatically from language corpora contain human-like biases (Caliskan et al., 2017)
Source Categories: borrowed-from-social-sciences
Link: https://dataverse.harvard.edu/dataset.xhtml?persistentId=doi:10.7910/DVN/DX4VWP
Seeds: [Todd, Neil, Geoffrey, Brett, Brendan, Greg, Matthew, Brad, Allison, Anne, Carrie, Emily, Jill, Laurie, Meredith, Sarah]

Seeds ID: african_american_names_market_discrimination-Caliskan_et_al_2017
Category: african american names market discrimination
Used in Paper: Semantics derived automatically from language corpora contain human-like biases (Caliskan et al., 2017)
Source Categories: borrowed-from-social-sciences
Link: https://dataverse.harvard.edu/dataset.xhtml?persistentId=doi:10.7910/DVN/DX4VWP
Seeds: [Kareem, Darnell, Tyrone, Hakim, Jamal, Leroy, Jermaine, Rasheed, Aisha, Ebony, Keisha, Kenya, Lakisha, Latoya, Tamika, Tanisha]

Seeds ID: pleasantness-Caliskan_et_al_2017
Category: pleasantness
Used in Paper: Semantics derived automatically from language corpora contain human-like biases (Caliskan et al., 2017)
Source Categories: borrowed-from-social-sciences
Link: https://dataverse.harvard.edu/dataset.xhtml?persistentId=doi:10.7910/DVN/DX4VWP
Seeds: [joy, love, peace, wonderful, pleasure, friend, laughter, happy]

Seeds ID: unpleasantness-Caliskan_et_al_2017
Category: unpleasantness
Used in Paper: Semantics derived automatically from language corpora contain human-like biases (Caliskan et al., 2017)
Source Categories: borrowed-from-social-sciences
Link: https://dataverse.harvard.edu/dataset.xhtml?persistentId=doi:10.7910/DVN/DX4VWP
Seeds: [agony, terrible, horrible, nasty, evil, war, awful, failure]

Seeds ID: male_names_1-Caliskan_et_al_2017
Category: male names 1
Used in Paper: Semantics derived automatically from language corpora contain human-like biases (Caliskan et al., 2017)
Source Categories: borrowed-from-social-sciences
Link: https://dataverse.harvard.edu/dataset.xhtml?persistentId=doi:10.7910/DVN/DX4VWP
Seeds: [John, Paul, Mike, Kevin, Steve, Greg, Jeff, Bill]

Seeds ID: female_names_1-Caliskan_et_al_2017
Category: female names 1
Used in Paper: Semantics derived automatically from language corpora contain human-like biases (Caliskan et al., 2017)
Source Categories: borrowed-from-social-sciences
Link: https://dataverse.harvard.edu/dataset.xhtml?persistentId=doi:10.7910/DVN/DX4VWP
Seeds: [Amy, Joan, Lisa, Sarah, Diana, Kate, Ann, Donna]

Seeds ID: career-Caliskan_et_al_2017
Category: career
Used in Paper: Semantics derived automatically from language corpora contain human-like biases (Caliskan et al., 2017)
Source Categories: borrowed-from-social-sciences
Link: https://dataverse.harvard.edu/dataset.xhtml?persistentId=doi:10.7910/DVN/DX4VWP
Seeds: [executive, management, professional, corporation, salary, office, business, career]

Seeds ID: family-Caliskan_et_al_2017
Category: family
Used in Paper: Semantics derived automatically from language corpora contain human-like biases (Caliskan et al., 2017)
Source Categories: borrowed-from-social-sciences
Link: https://dataverse.harvard.edu/dataset.xhtml?persistentId=doi:10.7910/DVN/DX4VWP
Seeds: [home, parents, children, family, cousins, marriage, wedding, relatives]

Seeds ID: math_1-Caliskan_et_al_2017
Category: math 1
Used in Paper: Semantics derived automatically from language corpora contain human-like biases (Caliskan et al., 2017)
Source Categories: borrowed-from-social-sciences
Link: https://dataverse.harvard.edu/dataset.xhtml?persistentId=doi:10.7910/DVN/DX4VWP
Seeds: [math, algebra, geometry, calculus, equations, computation, numbers, addition]

Seeds ID: arts_1-Caliskan_et_al_2017
Category: arts 1
Used in Paper: Semantics derived automatically from language corpora contain human-like biases (Caliskan et al., 2017)
Source Categories: borrowed-from-social-sciences
Link: https://dataverse.harvard.edu/dataset.xhtml?persistentId=doi:10.7910/DVN/DX4VWP
Seeds: [poetry, art, sculpture, dance, literature, novel, symphony, drama]

Seeds ID: male_1-Caliskan_et_al_2017
Category: male 1
Used in Paper: Semantics derived automatically from language corpora contain human-like biases (Caliskan et al., 2017)
Source Categories: borrowed-from-social-sciences
Link: https://dataverse.harvard.edu/dataset.xhtml?persistentId=doi:10.7910/DVN/DX4VWP
Seeds: [brother, male, man, boy, son, he, his, him]

Seeds ID: female_1-Caliskan_et_al_2017
Category: female 1
Used in Paper: Semantics derived automatically from language corpora contain human-like biases (Caliskan et al., 2017)
Source Categories: borrowed-from-social-sciences
Link: https://dataverse.harvard.edu/dataset.xhtml?persistentId=doi:10.7910/DVN/DX4VWP
Seeds: [sister, female, woman, girl, daughter, she, hers, her]

Seeds ID: science_1-Caliskan_et_al_2017
Category: science 1
Used in Paper: Semantics derived automatically from language corpora contain human-like biases (Caliskan et al., 2017)
Source Categories: borrowed-from-social-sciences
Link: https://dataverse.harvard.edu/dataset.xhtml?persistentId=doi:10.7910/DVN/DX4VWP
Seeds: [science, technology, physics, chemistry, Einstein, NASA, experiment, astronomy]

Seeds ID: arts_2-Caliskan_et_al_2017
Category: arts 2
Used in Paper: Semantics derived automatically from language corpora contain human-like biases (Caliskan et al., 2017)
Source Categories: borrowed-from-social-sciences
Link: https://dataverse.harvard.edu/dataset.xhtml?persistentId=doi:10.7910/DVN/DX4VWP
Seeds: [poetry, art, Shakespeare, dance, literature, novel, symphony, drama]

Seeds ID: male_2-Caliskan_et_al_2017
Category: male 2
Used in Paper: Semantics derived automatically from language corpora contain human-like biases (Caliskan et al., 2017)
Source Categories: borrowed-from-social-sciences
Link: https://dataverse.harvard.edu/dataset.xhtml?persistentId=doi:10.7910/DVN/DX4VWP
Seeds: [brother, father, uncle, grandfather, son, he, his, him]

Seeds ID: female_2-Caliskan_et_al_2017
Category: female 2
Used in Paper: Semantics derived automatically from language corpora contain human-like biases (Caliskan et al., 2017)
Source Categories: borrowed-from-social-sciences
Link: https://dataverse.harvard.edu/dataset.xhtml?persistentId=doi:10.7910/DVN/DX4VWP
Seeds: [sister, mother, aunt, grandmother, daughter, she, hers, her]

Seeds ID: careers-Caliskan_et_al_2017
Category: careers
Used in Paper: Semantics derived automatically from language corpora contain human-like biases (Caliskan et al., 2017)
Source Categories: population-derived
Link: https://dataverse.harvard.edu/dataset.xhtml?persistentId=doi:10.7910/DVN/DX4VWP
Seeds: [technician, accountant, supervisor, engineer, worker, educator, clerk, counselor, inspector, mechanic, manager, therapist, administrator, salesperson, receptionist, librarian, advisor, pharmacist, janitor, psychologist, physician, carpenter, nurse, investigator, bartender, specialist, electrician, officer, pathologist, teacher, lawyer, planner, practitioner, plumber, instructor, surgeon, veterinarian, paramedic, examiner, chemist, machinist, appraiser, nutritionist, architect, hairdresser, baker, programmer, paralegal, hygienist, scientist]

Seeds ID: androgynous_names-Caliskan_et_al_2017
Category: androgynous names
Used in Paper: Semantics derived automatically from language corpora contain human-like biases (Caliskan et al., 2017)
Source Categories: population-derived
Link: https://dataverse.harvard.edu/dataset.xhtml?persistentId=doi:10.7910/DVN/DX4VWP
Seeds: [Kelly, Tracy, Jamie, Jackie, Jesse, Courtney, Lynn, Taylor, Leslie, Shannon, Stacey, Jessie, Shawn, Stacy, Casey, Bobby, Terry, Lee, Ashley, Eddie, Chris, Jody, Pat, Carey, Willie, Morgan, Robbie, Joan, Alexis, Kris, Frankie, Bobbie, Dale, Robin, Billie, Adrian, Kim, Jaime, Jean, Francis, Marion, Dana, Rene, Johnnie, Jordan, Carmen, Ollie, Dominique, Jimmie, Shelby]

Seeds ID: depressed_1-Caliskan_et_al_2017
Category: depressed 1
Used in Paper: Semantics derived automatically from language corpora contain human-like biases (Caliskan et al., 2017)
Source Categories:
Link: https://dataverse.harvard.edu/dataset.xhtml?persistentId=doi:10.7910/DVN/DX4VWP
Seeds: [sad, hopeless, gloomy, tearful, miserable, depressed]

Seeds ID: physically_ill-Caliskan_et_al_2017
Category: physically ill
Used in Paper: Semantics derived automatically from language corpora contain human-like biases (Caliskan et al., 2017)
Source Categories:
Link: https://dataverse.harvard.edu/dataset.xhtml?persistentId=doi:10.7910/DVN/DX4VWP
Seeds: [sick, illness, influenza, disease, virus, cancer]

Seeds ID: temporary-Caliskan_et_al_2017
Category: temporary
Used in Paper: Semantics derived automatically from language corpora contain human-like biases (Caliskan et al., 2017)
Source Categories:
Link: https://dataverse.harvard.edu/dataset.xhtml?persistentId=doi:10.7910/DVN/DX4VWP
Seeds: [impermanent, unstable, variable, fleeting, short-term, brief, occasional]

Seeds ID: permanent-Caliskan_et_al_2017
Category: permanent
Used in Paper: Semantics derived automatically from language corpora contain human-like biases (Caliskan et al., 2017)
Source Categories:
Link: https://dataverse.harvard.edu/dataset.xhtml?persistentId=doi:10.7910/DVN/DX4VWP
Seeds: [stable, always, constant, persistent, chronic, prolonged, forever]

Seeds ID: young_names-Caliskan_et_al_2017
Category: young names
Used in Paper: Semantics derived automatically from language corpora contain human-like biases (Caliskan et al., 2017)
Source Categories:
Link: https://dataverse.harvard.edu/dataset.xhtml?persistentId=doi:10.7910/DVN/DX4VWP
Seeds: [Tiffany, Michelle, Cindy, Kristy, Brad, Eric, Joey, Billy]

Seeds ID: old_names-Caliskan_et_al_2017
Category: old names
Used in Paper: Semantics derived automatically from language corpora contain human-like biases (Caliskan et al., 2017)
Source Categories:
Link: https://dataverse.harvard.edu/dataset.xhtml?persistentId=doi:10.7910/DVN/DX4VWP
Seeds: [Ethel, Bernice, Gertrude, Agnes, Cecil, Wilbert, Mortimer, Edgar]

Seeds ID: pleasant_6-Caliskan_et_al_2017
Category: pleasant 6
Used in Paper: Semantics derived automatically from language corpora contain human-like biases (Caliskan et al., 2017)
Source Categories:
Link: https://dataverse.harvard.edu/dataset.xhtml?persistentId=doi:10.7910/DVN/DX4VWP
Seeds: [joy, love, peace, wonderful, pleasure, friend, laughter, happy]

Seeds ID: unpleasant_6-Caliskan_et_al_2017
Category: unpleasant 6
Used in Paper: Semantics derived automatically from language corpora contain human-like biases (Caliskan et al., 2017)
Source Categories:
Link: https://dataverse.harvard.edu/dataset.xhtml?persistentId=doi:10.7910/DVN/DX4VWP
Seeds: [agony, terrible, horrible, nasty, evil, war, awful, failure]

Seeds ID: definitional_female-Bolukbasi_et_al_2016
Category: definitional female
Used in Paper: Man is to Computer Programmer as Woman is to Homemaker? Debiasing Word Embeddings (Bolukbasi et al., 2016)
Source Categories: curated
Link: https://github.com/tolga-b/debiaswe
Seeds: [woman, girl, she, mother, daughter, gal, female, her, herself, Mary]

Seeds ID: definitional_male-Bolukbasi_et_al_2016
Category: definitional male
Used in Paper: Man is to Computer Programmer as Woman is to Homemaker? Debiasing Word Embeddings (Bolukbasi et al., 2016)
Source Categories: curated
Link: https://github.com/tolga-b/debiaswe
Seeds: [man, boy, he, father, son, guy, male, his, himself, John]

Seeds ID: equalize_1-Bolukbasi_et_al_2016
Category: equalize 1
Used in Paper: Man is to Computer Programmer as Woman is to Homemaker? Debiasing Word Embeddings (Bolukbasi et al., 2016)
Source Categories: corpus-derived
Link: https://github.com/tolga-b/debiaswe
Seeds: [monastery, spokesman, Catholic_priest, Dad, Men, councilman, grandpa, grandsons, prostate_cancer, testosterone, uncle, wives, Father, Grandpa, He, boy, boys, brother, brothers, businessman, chairman, colt, congressman, dad, dads, dudes, ex_girlfriend, father, fatherhood, fathers, fella, fraternity, gelding, gentleman, gentlemen, grandfather, grandson, he, himself, his, king, kings, male, males, man, men, nephew, prince, schoolboy, son, sons, twin_brother]

Seeds ID: equalize_2-Bolukbasi_et_al_2016
Category: equalize 2
Used in Paper: Man is to Computer Programmer as Woman is to Homemaker? Debiasing Word Embeddings (Bolukbasi et al., 2016)
Source Categories:
Link: https://github.com/tolga-b/debiaswe
Seeds: [convent, spokeswoman, nun, Mom, Women, councilwoman, grandma, granddaughters, ovarian_cancer, estrogen, aunt, husbands, Mother, Grandma, She, girl, girls, sister, sisters, businesswoman, chairwoman, filly, congresswoman, mom, moms, gals, ex_boyfriend, mother, motherhood, mothers, granny, sorority, mare, lady, ladies, grandmother, granddaughter, she, herself, her, queen, queens, female, females, woman, women, niece, princess, schoolgirl, daughter, daughters, twin_sister]

Seeds ID: gender_specific-Bolukbasi_et_al_2016
Category: gender specific
Used in Paper: Man is to Computer Programmer as Woman is to Homemaker? Debiasing Word Embeddings (Bolukbasi et al., 2016)
Source Categories:
Link: https://github.com/tolga-b/debiaswe
Seeds: [he, his, He, her, she, him, She, man, women, men, His, woman, spokesman, wife, himself, son, mother, father, chairman, daughter, husband, guy, girls, girl, Her, boy, King, boys, brother, Chairman, spokeswoman, female, sister, Women, Man, male, herself, Lions, Lady, brothers, dad, actress, mom, sons, girlfriend, Kings, Men, daughters, Prince, Queen, teenager, lady, Bulls, boyfriend, sisters, Colts, mothers, Sir, king, businessman, Boys, grandmother, grandfather, deer, cousin, Woman, ladies, Girls, Father, uncle, PA, Boy, Councilman, mum, Brothers, MA, males, Girl, Mom, Guy, Queens, congressman, Dad, Mother, grandson, twins, bull, queen, businessmen, wives, widow, nephew, bride, females, aunt, Congressman, prostate_cancer, lesbian, chairwoman, fathers, Son, moms, Ladies, maiden, granddaughter, younger_brother, Princess, Guys, lads, Ma, Sons, lion, Bachelor, gentleman, fraternity, bachelor, niece, Lion, Sister, bulls, husbands, prince, colt, salesman, Bull, Sisters, hers, dude, Spokesman, beard, filly, Actress, Him, princess, Brother, lesbians, councilman, actresses, Viagra, gentlemen, stepfather, Deer, monks, Beard, Uncle, ex_girlfriend, lad, sperm, Daddy, testosterone, MAN, Female, nephews, maid, daddy, mare, fiance, Wife, fiancee, kings, dads, waitress, Male, maternal, heroine, feminist, Mama, nieces, girlfriends, Councilwoman, sir, stud, Mothers, mistress, lions, estranged_wife, womb, Brotherhood, Statesman, grandma, maternity, estrogen, ex_boyfriend, widows, gelding, diva, teenage_girls, nuns, Daughter, czar, ovarian_cancer, HE, Monk, countrymen, Grandma, teenage_girl, penis, bloke, nun, Husband, brides, housewife, spokesmen, suitors, menopause, monastery, patriarch, Beau, motherhood, brethren, stepmother, Dude, prostate, Moms, hostess, twin_brother, Colt, schoolboy, eldest, brotherhood, Godfather, fillies, stepson, congresswoman, Chairwoman, Daughters, uncles, witch, Mommy, monk, viagra, paternity, suitor, chick, Pa, fianc\u00e9, sorority, macho, Spokeswoman, businesswoman, eldest_son, gal, statesman, schoolgirl, fathered, goddess, hubby, mares, stepdaughter, blokes, dudes, socialite, strongman, Witch, fianc\u00e9e, uterus, grandsons, Bride, studs, mama, Aunt, godfather, hens, hen, mommy, Babe, estranged_husband, Fathers, elder_brother, boyhood, baritone, Diva, Lesbian, grandmothers, grandpa, boyfriends, feminism, countryman, stallion, heiress, queens, Grandpa, witches, aunts, semen, fella, granddaughters, chap, knight, widower, Maiden, salesmen, convent, KING, vagina, beau, babe, HIS, beards, handyman, twin_sister, maids, gals, housewives, Gentlemen, horsemen, Businessman, obstetrics, fatherhood, beauty_queen, councilwoman, princes, matriarch, colts, manly, ma, fraternities, Spokesmen, pa, fellas, Gentleman, councilmen, dowry, barbershop, Monks, WOMAN, fraternal, ballerina, manhood, Dads, heroines, granny, gynecologist, princesses, Goddess, yo, Granny, knights, eldest_daughter, HER, underage_girls, masculinity, Girlfriend, bro, Grandmother, grandfathers, crown_prince, Restless, paternal, Queen_Mother, Boyfriend, womens, Males, SHE, Countess, stepchildren, Belles, bachelors, matron, momma, Legs, maidens, goddesses, landlady, sisterhood, Grandfather, Fraternity, Majesty, Babes, lass, maternal_grandmother, blondes, ma, am, Womens, divorcee, Momma, fathering, Effie, Lad, womanhood, missus, Sisterhood, granddad, Mens, papa, gf, sis, Husbands, Hen, womanizer, gynecological, stepsister, Handsome, Prince_Charming, BOY, stepdad, teen_ager, GIRL, dame, Sorority, beauty_pageants, raspy, harem, maternal_grandfather, Hes, deliveryman, septuagenarian, damsel, paternal_grandmother, paramour, paternal_grandparents, Nun, DAD, mothering, shes, HE_, S, Nuns, teenage_daughters, auntie, widowed_mother, Girlfriends, FATHER, virile, COUPLE, grandmas, Hubby, nan, vixen, Joan_Crawford, stepdaughters, endometrial_cancer, stepsons, loins, Grandson, Mitchells, erections, Matron, Fella, daddies, ter, Sweetie, Dudes, Princesses, Lads, lioness, Mamma, virility, bros, womenfolk, Heir, BROTHERS, manliness, patriarchs, earl, sisterly, Whore, Gynaecology, countess, convents, Oratory, witch_doctor, mamas, yah, aunty, aunties, Heiress, lasses, Breasts, fairer_sex, sorority_sisters, WIFE, Laurels, penile, nuh, mah, toms, mam, Granddad, premenopausal_women, Granddaddy, nana, coeds, dames, herdsman, Mammy, Fellas, Niece, menfolk, Grandad, bloods, Gramps, damsels, Granddaughter, mamma, concubine, Oros, Blarney, filial, broads, Ethel_Kennedy, ACTRESS, Tit, fianc, Hunk, Night_Shift, wifey, Lothario, Holy_Roman_Emperor, horse_breeder, grandnephew, Lewises, Muscular, feminist_movement, Sanan, women\u00e2_\u20ac_\u2122, Fiancee, dowries, Carmelite, rah, n_roller, bay_filly, belles, Uncles, PRINCESS, womans, Homeboy, Blokes, Charmer, codger, Delta_Zeta, courtesans, grandaughter, SISTER, Highness, grandbabies, crone, Skip_Away, noblewoman, bf, jane, philandering_husband, Sisqo, mammy, daugher, director_Skip_Bertman, DAUGHTER, Royal_Highness, mannish, spinsters, Missus, madame, Godfathers, saleswomen, beaus, Risha, luh, sah, negligee, Women\u00e2_\u20ac_\u2122, Hos, salesgirl, grandmom, Grandmas, Lawsons, countrywomen, Booby, darlin, Sheiks, boyz, wifes, Bayi, Il_Duce, \u00e2_\u20ac_\u0153My, fem, daugther, Potti, hussy, tch, Gelding, stemmed_roses, Damson, puh, Tylers, neice, Mutha, GRANDMOTHER, youse, spurned_lover, mae, Britt_Ekland, clotheshorse, Carlita_Kilpatrick, Cambest, Pretty_Polly, banshees, male_chauvinist, Arliss, mommas, maidservant, Gale_Harold, Little_Bo_Peep, Cleavers, hags, blowsy, Queen_Elizabeth_I., lassies, papas, BABE, ugly_ducklings, Jims, hellion, Beautician, coalminer, relaxin, El_Mahroug, Victoria_Secret_Angel, shepherdess, Mosco, Slacks, nanna, wifely, tomboys, LAH, hast, apo, Kaplans, milkmaid, Robin_Munis, John_Barleycorn, royal_highness, Meanie, NAH, trollop, roh, Jewess, Sheik_Hamad, mumsy, Big_Pussy, chil_dren, Aunt_Bea, basso, sista, girlies, nun_Sister, chica, Bubbas, massa, Southern_belles, Nephews, castrations, Mister_Ed, Grandsons, Calaf, Malachy_McCourt, Shamash, hey_hey, Harmen, sonofabitch, Donovans, Grannie, Kalinka, hisself, Devean, goatherd, hinds, El_Corredor, Kens, notorious_womanizer, goh, Mommas, washerwoman, Samaira, Coo_Coo, Governess, grandsire, PRINCE_WILLIAM, gramma, him.He, Coptic_priest, Corbie, Kennys, thathe, Pa_Pa, Bristols, Hotep, snowy_haired, El_Prado_Ire, Girl_hitmaker, Hurleys, St._Meinrad, sexually_perverted, authoress, Prudie, raven_haired_beauty, Bonos, domestic_shorthair, brothas, nymphet, Neelma, Seita, stud_muffin, St._Judes, yenta, bare_shouldered, Pinkney_Sr., PRINCE_CHARLES, Bisutti, sistas, Blanche_Devereaux, Momoa, Quiff, Scotswoman, balaclava_clad_men, Louis_Leakey, dearie, vacuum_cleaner_salesman, grandads, postulant, SARAH_JESSICA_PARKER, AUNT, Prince_Dauntless, Dalys, Darkie, Czar_Nicholas, Lion_Hearted, Boy_recliner, baby_mamas, giantess, Lawd, GRANNY, fianc_e, Bilqis, WCTU, famly, Ellas, feminazis, Pentheus, MAMAS, Town_Criers, Saggy, youngman, grandam, divorc\u00e9, bosomed, roon, Simmentals, eponymous_heroine, LEYLAND, REE, cain, t, Evelynn, WAH, sistah, Horners, Elsie_Poncher, Coochie, rat_terriers, Limousins, Buchinski, Schicchi, Carpitcher, Khwezi, HAH, Shazza, Mackeson, ROH, kuya, novice_nun, Shei, Elmasri, ladykiller, 6yo, Yenta, SHEL, pater, Souse, Tahirah, comedian_Rodney_Dangerfield, Shottle, carryin, Sath, fa, afafine, royal_consort, hus_band, maternal_uncles, dressing_provocatively, dreamgirl, millionaire_industrialist, Georgie_Girl, Must_Be_Obeyed, joh, Arabian_stallion, ahr, mso_para_margin_0in, SOO, Biddles, Chincoteague_Volunteer_Fire, Lisa_Miceli, gorgeous_brunette, fianc\u017d, Moved_fluently, Afternoon_Deelites, biker_dude, Vito_Spatafore, MICK_JAGGER, Adesida, Reineman, witz, Djamila, Glenroe, daddys, Romanzi, gentlewomen, Dandie_Dinmont_terrier, Excess_Ire, By_SYVJ_Staff, zan, CONFESSIONS, Magees, wimmin, tash, Theatrical_Ire, Prince_Charmings, chocolate_eclair, bron, daughers, Felly, fiftyish, Spritely, GRANDPA, distaffer, Norbertines, DAH, leader_Muammar_Gadaffi, swains, Prince_Tomohito, Honneur, Soeur, jouster, Pharaoh_Amenhotep_III, QUEEN_ELIZABETH_II, Ne, er, Galileo_Ire, Fools_Crow, Lannisters, Devines, gonzales, columnist_Ann_Landers, Moseleys, hiz, busch, roastee, toyboys, Sheffields, grandaunt, Galvins, Giongo, geh, flame_haired_actress, Grammarian, Greg_Evigan, frontierswoman, Debele, rabs, nymphets, aai, BREE, Shaqs, ZAY, pappa, Housa, refrigerator_repairman, artificial_inseminations, chickie, Rippa, teenager_Tracy_Turnblad, homebred_colt, Abigaille, hen_pecked_husband, businesman, her.She, Kaikeyi, Stittsworth, self_proclaimed_redneck, Khella, NeW, Evers_Swindell, Asmerom_Gebreselassie, Boy_recliners, Cliff_Claven, Legge_Bourke, Costos, d, honneur, sistahs, Cabble, sahn, CROW_AGENCY_Mont, jezebel, Harrolds, ROSARIO_DAWSON, INXS_frontman_Michael_Hutchence, Gursikh, Dadas, VIAGA, keen_horsewoman, Theodoric, Eldery, lihn, Alice_Kramden, Santarina, radical_cleric_al_Sadr, Curleys, SY, Fidaa, Saptapadi, Actor_Sean_Astin, Kellita_Smith, Doly, Libertina, Money_McBags, Chief_Bearhart, choirgirl, chestnut_stallion, VIGRA, BY_JIM_McCONNELL, Sal_Vitale, Trivia_buffs, kumaris, fraternal_lodge, galpals, Borino_Quinn, lina, LATEST_Rapper, Bezar, Manro, bakla, Grisetti, blond_bimbo, spinster_aunt, gurls, hiswife, paleface, Charlye, hippie_chicks, Khalifas, Picture_JUSTIN_SANSON, Hepburns, yez, ALDER, Sanussi, Lil_Sis, McLoughlins, Barbra_Jean, Lulua, thatshe, actress_Shohreh_Aghdashloo, SIR_ANTHONY_HOPKINS, Gloddy, ZAH, ORANGE, S, Danielle_Bimber, grandmum, Kulkis, Brazington, Marisa_Lenhard_CFA, SIR_JOHN, Clareman, Aqila, Heavily_tattooed, Libbys, thim, elocutionist, submissives, Inja, rahm, Agnes_Gooch, fake_tits, nancy_boys, Swaidan, SHAH, ain, ta_bed, Shumail_Raj, Duchesse, diethylstilbestrol_DES, colt_foal, unfaithful_lover, Maseri, nevah, SAHN, Barths, Toughkenamon, GUEST_STARS, him.But, Donna_Claspell, gingham_dresses, Massage_Parlour, wae, Wasacz, Magistra, vihl, Smriti_Iraani, boyish_haircut, workingwoman, borthers, Capuchin_friars, Nejma, yes_sirs, bivocational_pastor, Grafters, HOPWOOD, Nicknamed_Godzilla, yos, Berkenfield, Missis, sitcom_Designing_Women, Kafoa, trainer_Emma_Lavelle, sadomasochistic_dungeon, iht, desperates, predessor, wolf_cub, indigenous_Peruvians, Livia_Soprano, troh, colt_sired, BOND_HILL, ihl, Drydens, rahs, Piserchia, Sonny_Corinthos, bankrobber, Fwank, feisty_redhead, booze_guzzling, COOPERS, actress_Q, orianka_Kilcher, Cortezar, twe, Jacoub, Cindy_Iannarelli, Hell_Raiser, Fondly_referred, Bridal_Shoppe, Noleta, Christinas, IAGRA, LaTanya_Richardson, Sang_Bender, Assasins, sorrel_gelding, septugenarian, Hissy, Muqtada_al_Sadr_mook, Pfeni, MADRID_AFX_Banco_Santander, tuchis, LeVaughn, Gadzicki, transvestite_hooker, Fame_jockey_Laffit, nun_Sister_Mary, SAMSONOV, Mayflower_Madam, Shaque, well.He, Trainer_Julio_Canani, sorrel_mare, minivehicle_joint_venture, wife_Dwina, Aasiya_AH, see, Baratheon, Rick_O, Shay, Mammies, goatie, Nell_Gwynne, charmingly_awkward, Slamma, DEHL, Lorenzo_Borghese, ALMA_Wis., Anne_Scurria, father_Peruvians_alternately, JULIE_ANDREWS, Slim_Pickins, Victoria_Secret_stunner, BY, Sanam_Devdas, pronounced_luh, Pasha_Selim, \u4e2d\u534e, rson, maternal_grandmothers, IOWA_CITY_Ia, Madame_de_Tourvel, JAY, Sheika_Mozah_bint_Nasser, Hotsy_Totsy, D, Ginto, singer_Johnny_Paycheck, uterine_prolapse_surgery, SCOTTDALE_Pa., AdelaideNow_reports, Marcus_Schenkenberg, Clyse, Obiter_Dicta, comic_Sam_Kinison, bitties, ROCKVILLE_Ind., swimsuit_calendars, Decicio_Smith, Ma_ma, Rie_Miyazawa, celibate_chastity, gwah, ZAY, HER_Majesty, Defrere, Las_Madrinas, \u7c3f\u8042\u7ffb, Bea_Hamill, ARCADIA_Calif._Trainer, Bold_Badgett, stakes_victress, Hoppin_Frog, Narumiya, Flayfil, hardman_Vinnie_Jones, Marilyn_Monroe_lookalike, Kivanc_Tatlitug, Persis_Khambatta, SINKING_SPRING_Pa., len_3rd, DEAR_TRYING, Farndon_Cheshire, Krishna_Madiga, daughter_Princess_Chulabhorn, Marshall_Rooster_Cogburn, Kitty_Kiernan, Yokich, Jarou, Serdaris, ee_ay, Montifiore, Chuderewicz, Samuel_Le_Bihan, filly_Proud_Spell, Umm_Hiba, pronounced_koo, Sandy_Fonzo, KOR, Fielder_Civil_kisses, Federalsburg_Maryland, Nikah_ceremony, Brinke_Stevens, Yakama_Tribal_Council, Capuchin_Father, wife_Callista_Bisek, Beau_Dare, Bedoni, Arjun_Punj, JOHNNY_KNOXVILLE, cap_tain, Alderwood_Boys, Chi_Eta_Phi, ringleader_Charles_Graner, Savoies, Lalla_Salma, Mrs._Potiphar, fahn, name_Taylor_Sumers, Vernita_Green, Bollywood_baddie, BENBROOK_Texas, Assemblyman_Lou_Papan, virgin_brides, Cho_Eun, CATHY_Freeman, Uncle_Saul, Lao_Brewery, Ibo_tribe, ruf, rival_Edurne_Pasaban, Hei_Shangri_La, Mommy_dearest, interest_Angola_Sonogal, Ger_Monsun, PUSSYCAT_DOLL, Crown_Jewels_Condoms, Lord_Marke, Patootie, Nora_Bey, huntin_shootin, Minister_Raymond_Tshibanda, La_Nina_la_NEEN, signature_Whoppers, estranged_hubby_Kevin_Federline, UR, pill_poppin, GEHR, purebred_Arabians, husbandly_duties, VIAGRA_TIMING, Hereford_heifer, hushed_monotone_voice, Pola_Uddin, Wee_Jimmy_Krankie, Kwakwanso, Our_Galvinator, shoh, Codependency_Anonymous_Group, LA, Taufa, ahau, Invincible_Spirit_colt, SAH, _dur, MOUNT_CARMEL_Pa., watches_attentively, SNL_spinoffs, Seth_Nitschke, Duns_Berwickshire, defendant_Colleen_LaRose, Silky_O, Sullivan, Highcliff_Farm, REN, Comestar, Satisfied_Frog, Jai_Maharashtra, ATTICA_Ind., lover_Larry_Birkhead, Tami_Megal, chauvinist_pigs, Phi_sorority, Micronesian_immigrant, Lia_Boldt, Sugar_Tits, actress_Kathy_Najimy, zhoo, Colombo_underboss, Katsav_accusers, Bess_Houdini, rap_mogul_Diddy, companions_Khin_Khin, Van_Het, Mastoi_tribe, VITALY, ROLLING_STONES_rocker, womanizing_cad, LILY_COLE, paternal_grandfathers, Lt._Col._Kurt_Kosmatka, Kasseem_Jr., Ji_Ji, Wilburforce, VIAGRA_DOSE, English_Sheepdogs, pronounced_Kah, Htet_Htet_Oo, Brisk_Breeze, Eau_du, BY_MELANIE_EVANS, Neovasc_Medical, British_funnyman_RICKY, 4YO_mare, Hemaida, MONKTON, Mrs_Mujuru, BaGhana_BaGhana, Shaaban_Abdel_Rahim, Edward_Jazlowiecki_lawyer, Ajman_Stud, manly_pharaoh_even, Serra_Madeira_Islands, FRAY, panto_dames, Khin_Myo, dancer_Karima_El_Mahroug, CROWN_Princess, Baseball_HOFer, Hasta_la_Pasta, GIRLS_NEXT_DOOR, Benedict_Groeschel, Bousamra, Ruby_Rubacuori_Ruby, Monde_Bleu, Un_homme_qui, Taylor_Sumers, Rapper_EMINEM, Joe_Menchetti, VAY, supermodel_NAOMI_CAMPBELL, Supermodel_GISELE_BUNDCHEN, Au_Lait, Radar_Installed, THOMAS_TOWNSHIP_Mich., Rafinesque, Herman_Weinrich, Abraxas_Antelope, raspy_voiced_rocker, Manurewa_Cosmopolitan_Club, Paraone, THE_LEOPARD, Boy_Incorporated_LZB, Dansili_filly, Lumpy_Rutherford, unwedded_bliss, Bhavna_Sharma, Scarvagh, en_flagrante, Mottu_Maid, Dowager_Queen, NEEN, model_Monika_Zsibrita, ROSIE_PEREZ, Mattock_Ranger, Valorous, Surpreme, Marwari_businessmen, Grandparents_aunts, Kimberley_Vlaeminck, Lyn_Treece_Boys, PDX_Update, Virsa_Punjab, eyelash_fluttering, Pi_fraternity, HUNTLEIGH_Mo., novelist_Jilly_Cooper, Naha_Shuri_temple, Yasmine_Al_Massri, Mu_Gamma_Xi, Mica_Ertegun, Ocleppo, VIAGRA_CONTRAINDICATIONS, daughter_PEACHES, trainer_Geoff_Wragg, OVERNIGHT_DELIVERY, Fitts_retiree, de_Tourvel, Lil_Lad, north_easterner, Aol_Weird_News, Somewhat_improbably, Sikh_panth, Worcester_2m_7f, Zainab_Jah, OLYMPIC_medalist, Enoch_Petrucelly, collie_Lassie, LOW, clumsiness_Holloway, ayr, OHR, ROLLING_STONES_guitarist, LAH, _nee, Ian_Beefy_Botham, Awapuni_trainer, Glamorous_Granny, Chiang_Ching, MidAtlantic_Cardiovascular_Associates, Yeke, Seaforth_Huron_Expositor, Westley_Cary_Elwes, Cate_Blanchett_Veronica_Guerin, Bellas_Gate, witch_Glinda, wives_mistresses, Woodsville_Walmart, 2YO_colt, Manav_Sushant_Singh, Pupi_Avati_Il, Sigma_Beta_Rho, Bishop_Christopher_Senyonjo, Vodou_priest, Rubel_Chowdhury, Claddagh_Ring, TAH, _duh_al, al_Sadr_mook_TAH, ROBIN_GIBB, GAHN, BY_THOMAS_RANSON, sister_Carine_Jena, Lyphard_mare, summa_cum, Semenya_grandmother_Maputhi, Clare_Nuns, Talac, sex_hormones_androgens, majeste, Saint_Ballado_mare, Carrie_Huchel, Mae_Dok, wife_Dieula, Earnest_Sirls, spoof_bar_mitzvah, von_Boetticher, Audwin_Mosby, Case_presentationWe, Vincent_Papandrea, KRAY, Sergi_Benavent, Le_Poisson, Von_Cramm, Patti_Mell, Raymi_Coya, Benjamin_BeBe_Winans, Nana_Akosua, Auld_Acquaintance, Desire_Burunga, Company_Wrangler_Nestea, ask_Krisy_Plourde, JUANITA_BYNUM, livia, GAMB, Gail_Rosario_Dawson, Ramgarhia_Sikh, Catholic_nun_Sister, FOUR_WEDDINGS_AND, Robyn_Scherer, brother_King_Athelstan, Santo_Loquasto_Fences, Wee_Frees, MARISOL, Soliloquy_Stakes, Whatever_Spoetzl, Marc, Aurelio, mon_petit, Sabbar_al_Mashhadani, KAY, _lee, m_zah_MAH, BY_TAMI_ALTHOFF, hobbit_Samwise_Gamgee, Bahiya_Hariri_sister, daddy_Larry_Birkhead, Sow_Tracey_Ullman, coach_Viljo_Nousiainen, Carmen_Lebbos, conjoined_twins_Zainab, Rob_Komosa, ample_bosomed, Ageing_rocker, psychic_Oda]

Seeds ID: gender_specific_seed-Bolukbasi_et_al_2016
Category: gender specific seed
Used in Paper: Man is to Computer Programmer as Woman is to Homemaker? Debiasing Word Embeddings (Bolukbasi et al., 2016)
Source Categories:
Link: https://github.com/tolga-b/debiaswe
Seeds: [actress, actresses, aunt, aunts, bachelor, ballerina, barbershop, baritone, beard, beards, beau, bloke, blokes, boy, boyfriend, boyfriends, boyhood, boys, brethren, bride, brides, brother, brotherhood, brothers, bull, bulls, businessman, businessmen, businesswoman, chairman, chairwoman, chap, colt, colts, congressman, congresswoman, convent, councilman, councilmen, councilwoman, countryman, countrymen, czar, dad, daddy, dads, daughter, daughters, deer, diva, dowry, dude, dudes, elder_brother, eldest_son, estranged_husband, estranged_wife, estrogen, ex_boyfriend, ex_girlfriend, father, fathered, fatherhood, fathers, fella, fellas, female, females, feminism, fiance, fiancee, fillies, filly, fraternal, fraternities, fraternity, gal, gals, gelding, gentleman, gentlemen, girl, girlfriend, girlfriends, girls, goddess, godfather, granddaughter, granddaughters, grandfather, grandma, grandmother, grandmothers, grandpa, grandson, grandsons, guy, handyman, he, heiress, hen, hens, her, heroine, hers, herself, him, himself, his, horsemen, hostess, housewife, housewives, hubby, husband, husbands, king, kings, lad, ladies, lads, lady, lesbian, lesbians, lion, lions, ma, macho, maid, maiden, maids, male, males, mama, man, mare, maternal, maternity, matriarch, men, menopause, mistress, mom, mommy, moms, monastery, monk, monks, mother, motherhood, mothers, nephew, nephews, niece, nieces, nun, nuns, obstetrics, ovarian_cancer, pa, paternity, penis, prince, princes, princess, prostate, prostate_cancer, queen, queens, salesman, salesmen, schoolboy, schoolgirl, semen, she, sir, sister, sisters, son, sons, sorority, sperm, spokesman, spokesmen, spokeswoman, stallion, statesman, stepdaughter, stepfather, stepmother, stepson, strongman, stud, studs, suitor, suitors, teenage_girl, teenage_girls, testosterone, twin_brother, twin_sister, uncle, uncles, uterus, vagina, viagra, waitress, widow, widower, widows, wife, witch, witches, wives, woman, womb, women, younger_brother]

Seeds ID: male-Manzini_et_al_2019
Category: male
Used in Paper: Black is to Criminal as Caucasian is to Police: Detecting and Removing Multiclass Bias in Word Embeddings (Manzini et al., 2019)
Source Categories: prior-work
Link: https://github.com/TManzini/DebiasMulticlassWordEmbedding
Seeds: [he, his, son, father, male, boy, uncle]

Seeds ID: female-Manzini_et_al_2019
Category: female
Used in Paper: Black is to Criminal as Caucasian is to Police: Detecting and Removing Multiclass Bias in Word Embeddings (Manzini et al., 2019)
Source Categories: prior-work
Link: https://github.com/TManzini/DebiasMulticlassWordEmbedding
Seeds: [she, hers, daughter, mother, female, girl, aunt]

Seeds ID: male_roles-Manzini_et_al_2019
Category: male roles
Used in Paper: Black is to Criminal as Caucasian is to Police: Detecting and Removing Multiclass Bias in Word Embeddings (Manzini et al., 2019)
Source Categories: prior-work
Link: https://github.com/TManzini/DebiasMulticlassWordEmbedding
Seeds: [manager, executive, doctor, lawyer, programmer, scientist, soldier, supervisor, rancher, janitor, firefighter, officer]

Seeds ID: female_roles-Manzini_et_al_2019
Category: female roles
Used in Paper: Black is to Criminal as Caucasian is to Police: Detecting and Removing Multiclass Bias in Word Embeddings (Manzini et al., 2019)
Source Categories: prior-work
Link: https://github.com/TManzini/DebiasMulticlassWordEmbedding
Seeds: [secretary, nurse, clerk, artist, homemaker, dancer, singer, librarian, maid, hairdresser, stylist, receptionist, counselor]

Seeds ID: gender_test_terms-Manzini_et_al_2019
Category: gender test terms
Used in Paper: Black is to Criminal as Caucasian is to Police: Detecting and Removing Multiclass Bias in Word Embeddings (Manzini et al., 2019)
Source Categories: prior-work
Link: https://github.com/TManzini/DebiasMulticlassWordEmbedding
Seeds: [chair, house, supervisor, secretary, loud, weak]

Seeds ID: black-Manzini_et_al_2019
Category: black
Used in Paper: Black is to Criminal as Caucasian is to Police: Detecting and Removing Multiclass Bias in Word Embeddings (Manzini et al., 2019)
Source Categories: borrowed-from-social-sciences
Link: https://github.com/TManzini/DebiasMulticlassWordEmbedding
Seeds: [black, african, black, africa, africa, africa]

Seeds ID: caucasian-Manzini_et_al_2019
Category: caucasian
Used in Paper: Black is to Criminal as Caucasian is to Police: Detecting and Removing Multiclass Bias in Word Embeddings (Manzini et al., 2019)
Source Categories: borrowed-from-social-sciences
Link: https://github.com/TManzini/DebiasMulticlassWordEmbedding
Seeds: [caucasian, caucasian, white, america, america, europe]

Seeds ID: asian-Manzini_et_al_2019
Category: asian
Used in Paper: Black is to Criminal as Caucasian is to Police: Detecting and Removing Multiclass Bias in Word Embeddings (Manzini et al., 2019)
Source Categories: borrowed-from-social-sciences
Link: https://github.com/TManzini/DebiasMulticlassWordEmbedding
Seeds: [asian, asian, asian, asia, china, asia]

Seeds ID: black_roles-Manzini_et_al_2019
Category: black roles
Used in Paper: Black is to Criminal as Caucasian is to Police: Detecting and Removing Multiclass Bias in Word Embeddings (Manzini et al., 2019)
Source Categories: borrowed-from-social-sciences
Link: https://github.com/TManzini/DebiasMulticlassWordEmbedding
Seeds: [slave, musician, runner, criminal, homeless]

Seeds ID: caucasian_roles-Manzini_et_al_2019
Category: caucasian roles
Used in Paper: Black is to Criminal as Caucasian is to Police: Detecting and Removing Multiclass Bias in Word Embeddings (Manzini et al., 2019)
Source Categories: borrowed-from-social-sciences
Link: https://github.com/TManzini/DebiasMulticlassWordEmbedding
Seeds: [manager, executive, redneck, hillbilly, leader, farmer]

Seeds ID: asian_roles-Manzini_et_al_2019
Category: asian roles
Used in Paper: Black is to Criminal as Caucasian is to Police: Detecting and Removing Multiclass Bias in Word Embeddings (Manzini et al., 2019)
Source Categories: borrowed-from-social-sciences
Link: https://github.com/TManzini/DebiasMulticlassWordEmbedding
Seeds: [doctor, engineer, laborer, teacher]

Seeds ID: race_test_terms-Manzini_et_al_2019
Category: race test terms
Used in Paper: Black is to Criminal as Caucasian is to Police: Detecting and Removing Multiclass Bias in Word Embeddings (Manzini et al., 2019)
Source Categories: borrowed-from-social-sciences
Link: https://github.com/TManzini/DebiasMulticlassWordEmbedding
Seeds: [chair, house, smart, criminal, executive, farmer]

Seeds ID: jew-Manzini_et_al_2019
Category: jew
Used in Paper: Black is to Criminal as Caucasian is to Police: Detecting and Removing Multiclass Bias in Word Embeddings (Manzini et al., 2019)
Source Categories: borrowed-from-social-sciences
Link: https://github.com/TManzini/DebiasMulticlassWordEmbedding
Seeds: [judaism, jew, synagogue, torah, rabbi]

Seeds ID: christian-Manzini_et_al_2019
Category: christian
Used in Paper: Black is to Criminal as Caucasian is to Police: Detecting and Removing Multiclass Bias in Word Embeddings (Manzini et al., 2019)
Source Categories: borrowed-from-social-sciences
Link: https://github.com/TManzini/DebiasMulticlassWordEmbedding
Seeds: [christianity, christian, church, bible, priest]

Seeds ID: muslim-Manzini_et_al_2019
Category: muslim
Used in Paper: Black is to Criminal as Caucasian is to Police: Detecting and Removing Multiclass Bias in Word Embeddings (Manzini et al., 2019)
Source Categories: borrowed-from-social-sciences
Link: https://github.com/TManzini/DebiasMulticlassWordEmbedding
Seeds: [islam, muslim, mosque, quran, imam]

Seeds ID: jewish_attributes-Manzini_et_al_2019
Category: jewish attributes
Used in Paper: Black is to Criminal as Caucasian is to Police: Detecting and Removing Multiclass Bias in Word Embeddings (Manzini et al., 2019)
Source Categories: borrowed-from-social-sciences
Link: https://github.com/TManzini/DebiasMulticlassWordEmbedding
Seeds: [greedy, cheap, hairy, liberal]

Seeds ID: christian_attributes-Manzini_et_al_2019
Category: christian attributes
Used in Paper: Black is to Criminal as Caucasian is to Police: Detecting and Removing Multiclass Bias in Word Embeddings (Manzini et al., 2019)
Source Categories: borrowed-from-social-sciences
Link: https://github.com/TManzini/DebiasMulticlassWordEmbedding
Seeds: [judgemental, conservative, familial]

Seeds ID: muslim_attributes-Manzini_et_al_2019
Category: muslim attributes
Used in Paper: Black is to Criminal as Caucasian is to Police: Detecting and Removing Multiclass Bias in Word Embeddings (Manzini et al., 2019)
Source Categories: borrowed-from-social-sciences
Link: https://github.com/TManzini/DebiasMulticlassWordEmbedding
Seeds: [violent, terrorist, dirty, uneducated]

Seeds ID: religion_test_terms-Manzini_et_al_2019
Category: religion test terms
Used in Paper: Black is to Criminal as Caucasian is to Police: Detecting and Removing Multiclass Bias in Word Embeddings (Manzini et al., 2019)
Source Categories: borrowed-from-social-sciences
Link: https://github.com/TManzini/DebiasMulticlassWordEmbedding
Seeds: [chair, house, greedy, terrorist, dirty, greedy]

Seeds ID: religion_specific_terms-Manzini_et_al_2019
Category: religion specific terms
Used in Paper: Black is to Criminal as Caucasian is to Police: Detecting and Removing Multiclass Bias in Word Embeddings (Manzini et al., 2019)
Source Categories: borrowed-from-social-sciences
Link: https://github.com/TManzini/DebiasMulticlassWordEmbedding
Seeds: [synagogue, synagogues, altar, altars, parish, parishes, biblical, bishop, bishops, jihadist, clergy, bible, bibles, mosque, mosques, mullah, church, churches, sermon, sermons, papacy, imam, pew, chancel, pope, priest, priests, baptism, jihad, confessional, holy_eucharist, evangelical, jesus, burqa, vicar, vicars, judaism, christianity, islam, jew, christian, muslim, torah, quran, rabbi]

Seeds ID: adjectives_appearance-Garg_et_al_2018
Category: adjectives appearance
Used in Paper: Word Embeddings Quantify 100 Years of Gender and Ethnic Stereotypes (Garg et al., 2018)
Source Categories: other
Link: https://github.com/nikhgarg/EmbeddingDynamicStereotypes
Seeds: [alluring, voluptuous, blushing, homely, plump, sensual, gorgeous, slim, bald, athletic, fashionable, stout, ugly, muscular, slender, feeble, handsome, healthy, attractive, fat, weak, thin, pretty, beautiful, strong]

Seeds ID: adjectives_intelligence-Garg_et_al_2018
Category: adjectives intelligence
Used in Paper: Word Embeddings Quantify 100 Years of Gender and Ethnic Stereotypes (Garg et al., 2018)
Source Categories: other
Link: https://github.com/nikhgarg/EmbeddingDynamicStereotypes
Seeds: [precocious, resourceful, inquisitive, sagacious, inventive, astute, adaptable, reflective, discerning, intuitive, inquiring, judicious, analytical, luminous, venerable, imaginative, shrewd, thoughtful, sage, smart, ingenious, clever, brilliant, logical, intelligent, apt, genius, wise]

Seeds ID: adjectives_otherization-Garg_et_al_2018
Category: adjectives otherization
Used in Paper: Word Embeddings Quantify 100 Years of Gender and Ethnic Stereotypes (Garg et al., 2018)
Source Categories: borrowed-from-social-sciences
Link: https://github.com/nikhgarg/EmbeddingDynamicStereotypes
Seeds: [devious, bizarre, venomous, erratic, barbaric, frightening, deceitful, forceful, deceptive, envious, greedy, hateful, contemptible, brutal, monstrous, calculating, cruel, intolerant, aggressive, monstrous]

Seeds ID: adjectives_princeton-Garg_et_al_2018
Category: adjectives princeton
Used in Paper: Word Embeddings Quantify 100 Years of Gender and Ethnic Stereotypes (Garg et al., 2018)
Source Categories: borrowed-from-social-sciences
Link: https://github.com/nikhgarg/EmbeddingDynamicStereotypes
Seeds: [brilliant, intelligent, honest, alert, imaginative, artistic, industrious, kind, faithful, sportsmanlike, efficient, courteous, generous, ambitious, witty, individualistic, sensitive, progressive, straightforward, jovial, musical, neat, persistent, practical, scientific, sophisticated, meditative, loyal, pleasureloving, suave, happy-go-lucky, passionate, sensual, stolid, gregarious, traditional, methodical, religious, quiet, aggressive, shrewd, reserved, nationalistic, conservative, talkative, impulsive, ponderous, conventional, materialistic, radical, argumentative, frivolous, suggestible, sly, stubborn, imitative, naive, pugnacious, suspicious, evasive, loud, superstitious, mercenary, ostentatious, quicktempered, humorless, grasping, boastful, quarrelsome, gluttonous, slovenly, revengeful, arrogant, ignorant, dirty, conceited, stupid, cowardly, unreliable, treacherous, rude, deceitful, cruel]

Seeds ID: adjectives_sensitive-Garg_et_al_2018
Category: adjectives sensitive
Used in Paper: Word Embeddings Quantify 100 Years of Gender and Ethnic Stereotypes (Garg et al., 2018)
Source Categories: borrowed-from-social-sciences
Link: https://github.com/nikhgarg/EmbeddingDynamicStereotypes
Seeds: [inhibited, complacent, sensitive, mellow, solemn, studious, intelligent, brilliant, rational, serious, contemplative, cowardly, timid, shy, passive, delicate, gentle, soft, quiet, working]

Seeds ID: adjectives_williams_best-Garg_et_al_2018
Category: adjectives williams best
Used in Paper: Word Embeddings Quantify 100 Years of Gender and Ethnic Stereotypes (Garg et al., 2018)
Source Categories: borrowed-from-social-sciences
Link: https://github.com/nikhgarg/EmbeddingDynamicStereotypes
Seeds: [headstrong, thankless, tactful, distrustful, quarrelsome, effeminate, fickle, talkative, dependable, resentful, sarcastic, unassuming, changeable, resourceful, persevering, forgiving, assertive, individualistic, vindictive, sophisticated, deceitful, impulsive, sociable, methodical, idealistic, thrifty, outgoing, intolerant, autocratic, conceited, inventive, dreamy, appreciative, forgetful, forceful, submissive, pessimistic, versatile, adaptable, reflective, inhibited, outspoken, quitting, unselfish, immature, painstaking, leisurely, infantile, sly, praising, cynical, irresponsible, arrogant, obliging, unkind, wary, greedy, obnoxious, irritable, discreet, frivolous, cowardly, rebellious, adventurous, enterprising, unscrupulous, poised, moody, unfriendly, optimistic, disorderly, peaceable, considerate, humorous, worrying, preoccupied, trusting, mischievous, robust, superstitious, noisy, tolerant, realistic, masculine, witty, informal, prejudiced, reckless, jolly, courageous, meek, stubborn, aloof, sentimental, complaining, unaffected, cooperative, unstable, feminine, timid, retiring, relaxed, imaginative, shrewd, conscientious, industrious, hasty, commonplace, lazy, gloomy, thoughtful, dignified, wholesome, affectionate, aggressive, awkward, energetic, tough, shy, queer, careless, restless, cautious, polished, tense, suspicious, dissatisfied, ingenious, fearful, daring, persistent, demanding, impatient, contented, selfish, rude, spontaneous, conventional, cheerful, enthusiastic, modest, ambitious, alert, defensive, mature, coarse, charming, clever, shallow, deliberate, stern, emotional, rigid, mild, cruel, artistic, hurried, sympathetic, dull, civilized, loyal, withdrawn, confident, indifferent, conservative, foolish, moderate, handsome, helpful, gentle, dominant, hostile, generous, reliable, sincere, precise, calm, healthy, attractive, progressive, confused, rational, stable, bitter, sensitive, initiative, loud, thorough, logical, intelligent, steady, formal, complicated, cool, curious, reserved, silent, honest, quick, friendly, efficient, pleasant, severe, peculiar, quiet, weak, anxious, nervous, warm, slow, dependent, wise, organized, affected, reasonable, capable, active, independent, patient, practical, serious, understanding, cold, responsible, simple, original, strong, determined, natural, kind]

Seeds ID: female_pairs-Garg_et_al_2018
Category: female pairs
Used in Paper: Word Embeddings Quantify 100 Years of Gender and Ethnic Stereotypes (Garg et al., 2018)
Source Categories: curated
Link: https://github.com/nikhgarg/EmbeddingDynamicStereotypes
Seeds: [she, daughter, hers, her, mother, woman, girl, herself, female, sister, daughters, mothers, women, girls, females, sisters, aunt, aunts, niece, nieces]

Seeds ID: male_pairs-Garg_et_al_2018
Category: male pairs
Used in Paper: Word Embeddings Quantify 100 Years of Gender and Ethnic Stereotypes (Garg et al., 2018)
Source Categories: curated
Link: https://github.com/nikhgarg/EmbeddingDynamicStereotypes
Seeds: [he, son, his, him, father, man, boy, himself, male, brother, sons, fathers, men, boys, males, brothers, uncle, uncles, nephew, nephews]

Seeds ID: names_asian-Garg_et_al_2018
Category: names asian
Used in Paper: Word Embeddings Quantify 100 Years of Gender and Ethnic Stereotypes (Garg et al., 2018)
Source Categories: corpus-derived, population-derived
Link: https://github.com/nikhgarg/EmbeddingDynamicStereotypes
Seeds: [cho, wong, tang, huang, chu, chung, ng, wu, liu, chen, lin, yang, kim, chang, shah, wang, li, khan, singh, hong]

Seeds ID: names_black-Garg_et_al_2018
Category: names black
Used in Paper: Word Embeddings Quantify 100 Years of Gender and Ethnic Stereotypes (Garg et al., 2018)
Source Categories: corpus-derived, population-derived
Link: https://github.com/nikhgarg/EmbeddingDynamicStereotypes
Seeds: [harris, robinson, howard, thompson, moore, wright, anderson, clark, jackson, taylor, scott, davis, allen, adams, lewis, williams, jones, wilson, martin, johnson]

Seeds ID: names_chinese-Garg_et_al_2018
Category: names chinese
Used in Paper: Word Embeddings Quantify 100 Years of Gender and Ethnic Stereotypes (Garg et al., 2018)
Source Categories: corpus-derived, population-derived
Link: https://github.com/nikhgarg/EmbeddingDynamicStereotypes
Seeds: [chung, liu, wong, huang, ng, hu, chu, chen, lin, liang, wang, wu, yang, tang, chang, hong, li]

Seeds ID: names_hispanic-Garg_et_al_2018
Category: names hispanic
Used in Paper: Word Embeddings Quantify 100 Years of Gender and Ethnic Stereotypes (Garg et al., 2018)
Source Categories: corpus-derived, population-derived
Link: https://github.com/nikhgarg/EmbeddingDynamicStereotypes
Seeds: [ruiz, alvarez, vargas, castillo, gomez, soto, gonzalez, sanchez, rivera, mendoza, martinez, torres, rodriguez, perez, lopez, medina, diaz, garcia, castro, cruz]

Seeds ID: names_russian-Garg_et_al_2018
Category: names russian
Used in Paper: Word Embeddings Quantify 100 Years of Gender and Ethnic Stereotypes (Garg et al., 2018)
Source Categories: corpus-derived, population-derived
Link: https://github.com/nikhgarg/EmbeddingDynamicStereotypes
Seeds: [gurin, minsky, sokolov, markov, maslow, novikoff, mishkin, smirnov, orloff, ivanov, sokoloff, davidoff, savin, romanoff, babinski, sorokin, levin, pavlov, rodin, agin]

Seeds ID: names_white-Garg_et_al_2018
Category: names white
Used in Paper: Word Embeddings Quantify 100 Years of Gender and Ethnic Stereotypes (Garg et al., 2018)
Source Categories: corpus-derived, population-derived
Link: https://github.com/nikhgarg/EmbeddingDynamicStereotypes
Seeds: [harris, nelson, robinson, thompson, moore, wright, anderson, clark, jackson, taylor, scott, davis, allen, adams, lewis, williams, jones, wilson, martin, johnson]

Seeds ID: occuptations_1950_professional-Garg_et_al_2018
Category: occupations
Used in Paper: Word Embeddings Quantify 100 Years of Gender and Ethnic Stereotypes (Garg et al., 2018)
Source Categories: population-derived
Link: https://github.com/nikhgarg/EmbeddingDynamicStereotypes
Seeds: [janitor, statistician, midwife, bailiff, auctioneer, photographer, geologist, shoemaker, athlete, cashier, dancer, housekeeper, accountant, physicist, gardener, dentist, weaver, blacksmith, psychologist, supervisor, mathematician, surveyor, tailor, designer, economist, mechanic, laborer, postmaster, broker, chemist, librarian, attendant, clerical, musician, porter, scientist, carpenter, sailor, instructor, sheriff, pilot, inspector, mason, baker, administrator, architect, collector, operator, surgeon, driver, painter, conductor, nurse, cook, engineer, retired, sales, lawyer, clergy, physician, farmer, clerk, manager, guard, artist, smith, official, police, doctor, professor, student, judge, teacher, author, secretary, soldier]

Seeds ID: occupations_1950-Garg_et_al_2018
Category: occupations professional
Used in Paper: Word Embeddings Quantify 100 Years of Gender and Ethnic Stereotypes (Garg et al., 2018)
Source Categories: curated
Link: https://github.com/nikhgarg/EmbeddingDynamicStereotypes
Seeds: [statistician, auctioneer, photographer, geologist, accountant, physicist, dentist, psychologist, supervisor, mathematician, designer, economist, postmaster, broker, chemist, librarian, scientist, instructor, pilot, administrator, architect, surgeon, nurse, engineer, lawyer, physician, manager, official, doctor, professor, student, judge, teacher, author]

Seeds ID: occupations_mechanical_turk-Garg_et_al_2018
Category: occupations mechanical turk
Used in Paper: Word Embeddings Quantify 100 Years of Gender and Ethnic Stereotypes (Garg et al., 2018)
Source Categories: prior-work
Link: https://github.com/nikhgarg/EmbeddingDynamicStereotypes
Seeds: [instructor, geologist, secretary, clerk, painter, housekeeper, chemist, artist, baker, psychologist, lawyer, teacher, collector, surveyor, accountant, sailor, laborer, physician, student, soldier, manager, administrator, musician, doctor, dentist, professor, photographer, surgeon, inspector, janitor, nurse, author, conductor, economist, physicist, scientist, architect, mechanic, judge, gardener, farmer, librarian, carpenter, mathematician, dancer, broker, athlete]

Seeds ID: personality_traits_original-Garg_et_al_2018
Category: personality traits original
Used in Paper: Word Embeddings Quantify 100 Years of Gender and Ethnic Stereotypes (Garg et al., 2018)
Source Categories: borrowed-from-social-sciences
Link: https://github.com/nikhgarg/EmbeddingDynamicStereotypes
Seeds: [disorganized, devious, impressionable, circumspect, impassive, aimless, effeminate, unfathomable, fickle, unprincipled, inoffensive, reactive, providential, resentful, bizarre, impractical, sarcastic, misguided, imitative, pedantic, venomous, erratic, insecure, resourceful, neurotic, forgiving, profligate, whimsical, assertive, incorruptible, individualistic, faithless, disconcerting, barbaric, hypnotic, vindictive, observant, dissolute, frightening, complacent, boisterous, pretentious, disobedient, tasteless, sedentary, sophisticated, regimental, mellow, deceitful, impulsive, playful, sociable, methodical, willful, idealistic, boyish, callous, pompous, unchanging, crafty, punctual, compassionate, intolerant, challenging, scornful, possessive, conceited, imprudent, dutiful, lovable, disloyal, dreamy, appreciative, forgetful, unrestrained, forceful, submissive, predatory, fanatical, illogical, tidy, aspiring, studious, adaptable, conciliatory, artful, thoughtless, deceptive, frugal, reflective, insulting, unreliable, stoic, hysterical, rustic, inhibited, outspoken, unhealthy, ascetic, skeptical, painstaking, contemplative, leisurely, sly, mannered, outrageous, lyrical, placid, cynical, irresponsible, vulnerable, arrogant, persuasive, perverse, steadfast, crisp, envious, naive, greedy, presumptuous, obnoxious, irritable, dishonest, discreet, sporting, hateful, ungrateful, frivolous, reactionary, skillful, cowardly, sordid, adventurous, dogmatic, intuitive, bland, indulgent, discontented, dominating, articulate, fanciful, discouraging, treacherous, repressed, moody, sensual, unfriendly, optimistic, clumsy, contemptible, focused, haughty, morbid, disorderly, considerate, humorous, preoccupied, airy, impersonal, cultured, trusting, respectful, scrupulous, scholarly, superstitious, tolerant, realistic, malicious, irrational, sane, colorless, masculine, witty, inert, prejudiced, fraudulent, blunt, childish, brittle, disciplined, responsive, courageous, bewildered, courteous, stubborn, aloof, sentimental, athletic, extravagant, brutal, manly, cooperative, unstable, youthful, timid, amiable, retiring, fiery, confidential, relaxed, imaginative, mystical, shrewd, conscientious, monstrous, grim, questioning, lazy, dynamic, gloomy, troublesome, abrupt, eloquent, dignified, hearty, gallant, benevolent, maternal, paternal, patriotic, aggressive, competitive, elegant, flexible, gracious, energetic, tough, contradictory, shy, careless, cautious, polished, sage, tense, caring, suspicious, sober, neat, transparent, disturbing, passionate, obedient, crazy, restrained, fearful, daring, prudent, demanding, impatient, cerebral, calculating, amusing, honorable, casual, sharing, selfish, ruined, spontaneous, admirable, conventional, cheerful, solitary, upright, stiff, enthusiastic, petty, dirty, subjective, heroic, stupid, modest, impressive, orderly, ambitious, protective, silly, alert, destructive, exciting, crude, ridiculous, subtle, mature, creative, coarse, passive, oppressed, accessible, charming, clever, decent, miserable, superficial, shallow, stern, winning, balanced, emotional, rigid, invisible, desperate, cruel, romantic, agreeable, hurried, sympathetic, solemn, systematic, vague, peaceful, humble, dull, expedient, loyal, decisive, arbitrary, earnest, confident, conservative, foolish, moderate, helpful, delicate, gentle, dedicated, hostile, generous, reliable, dramatic, precise, calm, healthy, attractive, artificial, progressive, odd, confused, rational, brilliant, intense, genuine, mistaken, driving, stable, objective, sensitive, neutral, strict, angry, profound, smooth, ignorant, thorough, logical, intelligent, extraordinary, experimental, steady, formal, faithful, curious, reserved, honest, busy, educated, liberal, friendly, efficient, sweet, surprising, mechanical, clean, critical, criminal, soft, proud, quiet, weak, anxious, solid, complex, grand, warm, slow, false, extreme, narrow, dependent, wise, organized, pure, directed, dry, obvious, popular, capable, secure, active, independent, ordinary, fixed, practical, serious, fair, understanding, constant, cold, responsible, deep, religious, private, simple, physical, original, working, strong, modern, determined, open, political, difficult, knowledge, kind]

Seeds ID: christianity-Garg_et_al_2018
Category: christianity
Used in Paper: Word Embeddings Quantify 100 Years of Gender and Ethnic Stereotypes (Garg et al., 2018)
Source Categories: unknown
Link: https://github.com/nikhgarg/EmbeddingDynamicStereotypes
Seeds: [baptism, messiah, catholicism, resurrection, christianity, salvation, protestant, gospel, trinity, jesus, christ, christian, cross, catholic, church]

Seeds ID: islam-Garg_et_al_2018
Category: islam
Used in Paper: Word Embeddings Quantify 100 Years of Gender and Ethnic Stereotypes (Garg et al., 2018)
Source Categories: unknown
Link: https://github.com/nikhgarg/EmbeddingDynamicStereotypes
Seeds: [allah, ramadan, turban, emir, salaam, sunni, koran, imam, sultan, prophet, veil, ayatollah, shiite, mosque, islam, sheik, muslim, muhammad]

Seeds ID: terrorism-Garg_et_al_2018
Category: terrorism
Used in Paper: Word Embeddings Quantify 100 Years of Gender and Ethnic Stereotypes (Garg et al., 2018)
Source Categories: unknown
Link: https://github.com/nikhgarg/EmbeddingDynamicStereotypes
Seeds: [terror, terrorism, violence, attack, death, military, war, radical, injuries, bomb, target, conflict, dangerous, kill, murder, strike, dead, violence, fight, death, force, stronghold, wreckage, aggression, slaughter, execute, overthrow, casualties, massacre, retaliation, proliferation, militia, hostility, debris, acid, execution, militant, rocket, guerrilla, sacrifice, enemy, soldier, terrorist, missile, hostile, revolution, resistance, shoot]

Seeds ID: occupations-Kozlowski_et_al_2019
Category: occupations
Used in Paper: The Geometry of Culture: Analyzing Meaning through Word Embeddings (Kozlowski et al., 2019)
Source Categories: crowd-sourced, curated
Link:
Seeds: [banker, carpenter, doctor, engineer, hairdresser, journalist, lawyer, nanny, nurse, plumber, scientist]

Seeds ID: clothing-Kozlowski_et_al_2019
Category: clothing
Used in Paper: The Geometry of Culture: Analyzing Meaning through Word Embeddings (Kozlowski et al., 2019)
Source Categories: crowd-sourced, curated
Link:
Seeds: [blouse, briefcase, dress, necklace, pants, shirt, shorts, socks, suit, tuxedo]

Seeds ID: sports-Kozlowski_et_al_2019
Category: sports
Used in Paper: The Geometry of Culture: Analyzing Meaning through Word Embeddings (Kozlowski et al., 2019)
Source Categories: crowd-sourced, curated
Link:
Seeds: [baseball, basketball, boxing, golf, hockey, soccer, softball, tennis, volleyball]

Seeds ID: music_genres-Kozlowski_et_al_2019
Category: music genres
Used in Paper: The Geometry of Culture: Analyzing Meaning through Word Embeddings (Kozlowski et al., 2019)
Source Categories: crowd-sourced, curated
Link:
Seeds: [bluegrass, hiphop, jazz, opera, punk, rap, techno]

Seeds ID: vehicles-Kozlowski_et_al_2019
Category: vehicles
Used in Paper: The Geometry of Culture: Analyzing Meaning through Word Embeddings (Kozlowski et al., 2019)
Source Categories: crowd-sourced, curated
Link:
Seeds: [bicycle, limousine, minivan, motorcycle, skateboard, suv, truck]

Seeds ID: food-Kozlowski_et_al_2019
Category: food
Used in Paper: The Geometry of Culture: Analyzing Meaning through Word Embeddings (Kozlowski et al., 2019)
Source Categories: crowd-sourced, curated
Link:
Seeds: [beer, cheesecake, hamburger, pastry, salad, steak]

Seeds ID: first_names-Kozlowski_et_al_2019
Category: first names
Used in Paper: The Geometry of Culture: Analyzing Meaning through Word Embeddings (Kozlowski et al., 2019)
Source Categories: crowd-sourced, curated
Link:
Seeds: [Aaliyah, Amy, Connor, Jake, Jamal, Molly, Shanice]

Seeds ID: male-Kozlowski_et_al_2019
Category: male
Used in Paper: The Geometry of Culture: Analyzing Meaning through Word Embeddings (Kozlowski et al., 2019)
Source Categories: curated
Link:
Seeds: [man, men, he, him, his, his, boy, boys, male, masculine]

Seeds ID: female-Kozlowski_et_al_2019
Category: female
Used in Paper: The Geometry of Culture: Analyzing Meaning through Word Embeddings (Kozlowski et al., 2019)
Source Categories: curated
Link:
Seeds: [woman, women, she, her, her, hers, girl, girls, female, feminine]

Seeds ID: upperclass-Kozlowski_et_al_2019
Category: upperclass
Used in Paper: The Geometry of Culture: Analyzing Meaning through Word Embeddings (Kozlowski et al., 2019)
Source Categories: curated
Link:
Seeds: [rich, richer, richest, affluence, affluent, expensive, luxury, opulent]

Seeds ID: lowerclass-Kozlowski_et_al_2019
Category: lowerclass
Used in Paper: The Geometry of Culture: Analyzing Meaning through Word Embeddings (Kozlowski et al., 2019)
Source Categories: curated
Link:
Seeds: [poor, poorer, poorest, poverty, impoverished, inexpensive, cheap, needy]

Seeds ID: black-Kozlowski_et_al_2019
Category: black
Used in Paper: The Geometry of Culture: Analyzing Meaning through Word Embeddings (Kozlowski et al., 2019)
Source Categories: curated
Link:
Seeds: [black, blacks, Blacks, Black, African, African]

Seeds ID: white-Kozlowski_et_al_2019
Category: white
Used in Paper: The Geometry of Culture: Analyzing Meaning through Word Embeddings (Kozlowski et al., 2019)
Source Categories: curated
Link:
Seeds: [white, whites, Whites, White, European, Caucasian]

Seeds ID: female_names-Gonen_&_Goldberg_2019
Category: female names
Used in Paper: Lipstick on a Pig: Debiasing Methods Cover up Systematic Gender Biases in Word Embeddings But do not Remove Them (Gonen & Goldberg, 2019)
Source Categories: prior-work
Link:
Seeds: [Amy, Joan, Lisa, Sarah, Diana, Kate, Ann, Donna]

Seeds ID: male_names-Gonen_&_Goldberg_2019
Category: male names
Used in Paper: Lipstick on a Pig: Debiasing Methods Cover up Systematic Gender Biases in Word Embeddings But do not Remove Them (Gonen & Goldberg, 2019)
Source Categories: prior-work
Link:
Seeds: [John, Paul, Mike, Kevin, Steve, Greg, Jeff, Bill]

Seeds ID: family_words-Gonen_&_Goldberg_2019
Category: family words
Used in Paper: Lipstick on a Pig: Debiasing Methods Cover up Systematic Gender Biases in Word Embeddings But do not Remove Them (Gonen & Goldberg, 2019)
Source Categories: prior-work
Link:
Seeds: [home, parents, children, family, cousins, marriage, wedding, relatives]

Seeds ID: career_words-Gonen_&_Goldberg_2019
Category: career words
Used in Paper: Lipstick on a Pig: Debiasing Methods Cover up Systematic Gender Biases in Word Embeddings But do not Remove Them (Gonen & Goldberg, 2019)
Source Categories: prior-work
Link:
Seeds: [executive, management, professional, corpo-, ration, salary, office, business, career]

Seeds ID: arts_words-Gonen_&_Goldberg_2019
Category: arts words
Used in Paper: Lipstick on a Pig: Debiasing Methods Cover up Systematic Gender Biases in Word Embeddings But do not Remove Them (Gonen & Goldberg, 2019)
Source Categories: prior-work
Link:
Seeds: [poetry, art, dance, literature, novel, symphony, drama, sculpture]

Seeds ID: math_words-Gonen_&_Goldberg_2019
Category: math words
Used in Paper: Lipstick on a Pig: Debiasing Methods Cover up Systematic Gender Biases in Word Embeddings But do not Remove Them (Gonen & Goldberg, 2019)
Source Categories: prior-work
Link:
Seeds: [math, algebra, geometry, calculus, equations, computation, numbers, addition]

Seeds ID: arts_words_2-Gonen_&_Goldberg_2019
Category: arts words 2
Used in Paper: Lipstick on a Pig: Debiasing Methods Cover up Systematic Gender Biases in Word Embeddings But do not Remove Them (Gonen & Goldberg, 2019)
Source Categories: prior-work
Link:
Seeds: [poetry, art, Shakespeare, dance, literature, novel, symphony, drama]

Seeds ID: science_words-Gonen_&_Goldberg_2019
Category: science words
Used in Paper: Lipstick on a Pig: Debiasing Methods Cover up Systematic Gender Biases in Word Embeddings But do not Remove Them (Gonen & Goldberg, 2019)
Source Categories: prior-work
Link:
Seeds: [science, technology, physics, chemistry, Einstein, NASA, experiment, astronomy]

Seeds ID: sentiment_lexicon-Sweeney_&_Najafian_2019
Category: sentiment lexicon
Used in Paper: A Transparent Framework for Evaluating Unintended Demographic Bias in Word Embeddings (Sweeney & Najafian, 2019)
Source Categories: lexical-resources
Link:
Seeds: []

Seeds ID: neutral_identity_terms-Sweeney_&_Najafian_2019
Category: neutral identity terms
Used in Paper: A Transparent Framework for Evaluating Unintended Demographic Bias in Word Embeddings (Sweeney & Najafian, 2019)
Source Categories: corpus-derived
Link:
Seeds: []

Seeds ID: female_definition_words_1-Zhao_et_al_2018
Category: female definition words 1
Used in Paper: Learning gender-neutral word embeddings (Zhao et al., 2018)
Source Categories: corpus-derived
Link: https://github.com/uclanlp/gn_glove
Seeds: [countrywoman, sororal, witches, maidservant, mothers, diva, actress, spinster, mama, duchesses, barwoman, countrywomen, dowry, hostesses, airwomen, menopause, clitoris, princess, governesses, abbess, women, widow, ladies, sorceresses, madam, brides, baroness, housewives, godesses, niece, widows, lady, sister, brides, nun, adultresses, obstetrics, bellgirls, her, marchioness, princesses, empresses, mare, chairwoman, convent, priestesses, girlhood, ladies, queen, gals, mommies, maid, female_ejaculation, spokeswoman, seamstress, cowgirls, chick, spinsters, hair_salon, empress, mommy, feminism, gals, enchantress, gal, motherhood, estrogen, camerawomen, godmother, strongwoman, goddess, matriarch, aunt, chairwomen, ma, am, sisterhood, hostess, estradiol, wife, mom, stewardess, females, viagra, spokeswomen, ma, belle, minx, maiden, witch, miss, nieces, mothered, cow, belles, councilwomen, landladies, granddaughter, fiancees, stepmothers, horsewomen, grandmothers, adultress, schoolgirl, hen, granddaughters, bachelorette, camerawoman, moms, her, mistress, lass, policewoman, nun, actresses, saleswomen, girlfriend, councilwoman, lady, stateswoman, maternal, lass, landlady, sistren, ladies, wenches, sorority, bellgirl, duchess, ballerina, chicks, fiancee, fillies, wives, suitress, maternity, she, businesswoman, masseuses, heroine, doe, busgirls, girlfriends, queens, sisters, mistresses, stepmother, brides, daughter, minxes, cowgirl, lady, daughters, mezzo, saleswoman, mistress, hostess, nuns, maids, mrs., headmistresses, lasses, congresswoman, airwoman, housewife, priestess, barwomen, barnoesses, abbesses, handywoman, toque, sororities, stewardesses, filly, czarina, stepdaughters, herself, girls, lionesses, lady, vagina, hers, masseuse, cows, aunts, wench, toques, wife, lioness, sorceress, effeminate, mother, lesbians, female, waitresses, ovum, skene_gland, stepdaughter, womb, businesswomen, heiress, waitress, headmistress, woman, governess, godess, bride, grandma, bride, gal, lesbian, ladies, girl, grandmother, mare, maternity, hens, uterus, nuns, maidservants, seamstress, busgirl, heroines]

Seeds ID: male_definition_words_1-Zhao_et_al_2018
Category: male definition words 1
Used in Paper: Learning gender-neutral word embeddings (Zhao et al., 2018)
Source Categories: corpus-derived
Link: https://github.com/uclanlp/gn_glove
Seeds: [countryman, fraternal, wizards, manservant, fathers, divo, actor, bachelor, papa, dukes, barman, countrymen, brideprice, hosts, airmen, andropause, penis, prince, governors, abbot, men, widower, gentlemen, sorcerers, sir, bridegrooms, baron, househusbands, gods, nephew, widowers, lord, brother, grooms, priest, adultors, andrology, bellboys, his, marquis, princes, emperors, stallion, chairman, monastery, priests, boyhood, fellas, king, dudes, daddies, manservant, semen, spokesman, tailor, cowboys, dude, bachelors, barbershop, emperor, daddy, masculism, guys, enchanter, guy, fatherhood, androgen, cameramen, godfather, strongman, god, patriarch, uncle, chairmen, sir, brotherhood, host, testosterone, husband, dad, steward, males, cialis, spokesmen, pa, beau, stud, bachelor, wizard, sir, nephews, fathered, bull, beaus, councilmen, landlords, grandson, fiances, stepfathers, horsemen, grandfathers, adultor, schoolboy, rooster, grandsons, bachelor, cameraman, dads, him, master, lad, policeman, monk, actors, salesmen, boyfriend, councilman, fella, statesman, paternal, chap, landlord, brethren, lords, blokes, fraternity, bellboy, duke, ballet_dancer, dudes, fiance, colts, husbands, suitor, paternity, he, businessman, masseurs, hero, deer, busboys, boyfriends, kings, brothers, masters, stepfather, grooms, son, studs, cowboy, mentleman, sons, baritone, salesman, paramour, male_host, monks, menservants, mr., headmasters, lads, congressman, airman, househusband, priest, barmen, barons, abbots, handyman, beard, fraternities, stewards, colt, czar, stepsons, himself, boys, lions, gentleman, penis, his, masseur, bulls, uncles, bloke, beards, hubby, lion, sorcerer, macho, father, gays, male, waiters, sperm, prostate, stepson, prostatic_utricle, businessmen, heir, waiter, headmaster, man, governor, god, bridegroom, grandpa, groom, dude, gay, gents, boy, grandfather, gelding, paternity, roosters, prostatic_utricle, priests, manservants, stailor, busboy, heros]

Seeds ID: professions-Zhao_et_al_2018
Category: professions
Used in Paper: Learning gender-neutral word embeddings (Zhao et al., 2018)
Source Categories: prior-work
Link: https://github.com/uclanlp/gn_glove
Seeds: []

Seeds ID: male_definition_words_2-Zhao_et_al_2018
Category: male definition words 2
Used in Paper: Learning gender-neutral word embeddings (Zhao et al., 2018)
Source Categories: lexical-resources
Link: https://github.com/uclanlp/gn_glove
Seeds: [rake, wizard, policeman, host, councilman, actor, waiter, businessman, fiance, spokesman, salesman, widower, horseman, governor, statesman, hero, chairman, headmaster, priest, gentleman, countrymen, nobleman]

Seeds ID: female_definition_words_2-Zhao_et_al_2018
Category: female definition words 2
Used in Paper: Learning gender-neutral word embeddings (Zhao et al., 2018)
Source Categories: lexical-resources
Link: https://github.com/uclanlp/gn_glove
Seeds: [lady, saleswoman, noblewoman, hostess, coquette, nun, heroine, actress, chairwoman, businesswoman, spokeswoman, waitress, councilwoman, stateswoman, policewoman, countrywomen, horsewoman, headmistress, governess, widow, witch, fiancee]

Seeds ID: male_stereotype_words-Zhao_et_al_2018
Category: male stereotype words
Used in Paper: Learning gender-neutral word embeddings (Zhao et al., 2018)
Source Categories: lexical-resources
Link: https://github.com/uclanlp/gn_glove
Seeds: [researcher, lawyer, developer, architect, dentist, doctor, boss, chef, programmer, president, pilot, guard, warrior, judge, janitor, captain, engineer, dispatcher, leader, manager]

Seeds ID: female_stereotype_words-Zhao_et_al_2018
Category: female stereotype words
Used in Paper: Learning gender-neutral word embeddings (Zhao et al., 2018)
Source Categories: lexical-resources
Link: https://github.com/uclanlp/gn_glove
Seeds: [baker, counselor, nanny, librarians, socialite, assistant, tailor, dancer, hairdresser, cashier, secretary, clerk, stenographer, optometrist, housekeeper, bookkeeper, homemaker, nurse, stylist, receptionist]

Seeds ID: female-Rudinger_et_al_2017
Category: female
Used in Paper: Social Bias in Elicited Natural Language Inferences (Rudinger et al., 2017)
Source Categories: corpus-derived
Link: https://github.com/cjmay/snli-ethics
Seeds: [woman, women, her, her, she]

Seeds ID: male-Rudinger_et_al_2017
Category: male
Used in Paper: Social Bias in Elicited Natural Language Inferences (Rudinger et al., 2017)
Source Categories: corpus-derived
Link: https://github.com/cjmay/snli-ethics
Seeds: [man, men, him, his, he]

Seeds ID: black-Rudinger_et_al_2017
Category: black
Used in Paper: Social Bias in Elicited Natural Language Inferences (Rudinger et al., 2017)
Source Categories: corpus-derived
Link: https://github.com/cjmay/snli-ethics
Seeds: [black, black_person, black_man, black_woman]

Seeds ID: white-Rudinger_et_al_2017
Category: white
Used in Paper: Social Bias in Elicited Natural Language Inferences (Rudinger et al., 2017)
Source Categories: corpus-derived
Link: https://github.com/cjmay/snli-ethics
Seeds: [white, white_person, white_man, white_woman]

Seeds ID: hispanic-Rudinger_et_al_2017
Category: hispanic
Used in Paper: Social Bias in Elicited Natural Language Inferences (Rudinger et al., 2017)
Source Categories: corpus-derived
Link: https://github.com/cjmay/snli-ethics
Seeds: [hispanic, hispanic_person, hispanic_man, hispanic_woman]

Seeds ID: asian-Rudinger_et_al_2017
Category: asian
Used in Paper: Social Bias in Elicited Natural Language Inferences (Rudinger et al., 2017)
Source Categories: corpus-derived
Link: https://github.com/cjmay/snli-ethics
Seeds: [asian, asian_person, asian_man, asian_woman]

Seeds ID: career-Rudinger_et_al_2017
Category: career
Used in Paper: Social Bias in Elicited Natural Language Inferences (Rudinger et al., 2017)
Source Categories: corpus-derived
Link: https://github.com/cjmay/snli-ethics
Seeds: [salary, career, business, office, professional, management, corporation, executive]

Seeds ID: violence-Rudinger_et_al_2017
Category: violence
Used in Paper: Social Bias in Elicited Natural Language Inferences (Rudinger et al., 2017)
Source Categories: corpus-derived
Link: https://github.com/cjmay/snli-ethics
Seeds: [fight, fights, gun, guns, shoots, attacks, dangerous]

Seeds ID: female_2-Rudinger_et_al_2017
Category: female 2
Used in Paper: Social Bias in Elicited Natural Language Inferences (Rudinger et al., 2017)
Source Categories: corpus-derived
Link: https://github.com/cjmay/snli-ethics
Seeds: [woman, women, girl, girls, mother]

Seeds ID: male_2-Rudinger_et_al_2017
Category: male 2
Used in Paper: Social Bias in Elicited Natural Language Inferences (Rudinger et al., 2017)
Source Categories: corpus-derived
Link: https://github.com/cjmay/snli-ethics
Seeds: [man, men, boy, boys, father]

Seeds ID: old-Rudinger_et_al_2017
Category: old
Used in Paper: Social Bias in Elicited Natural Language Inferences (Rudinger et al., 2017)
Source Categories: corpus-derived
Link: https://github.com/cjmay/snli-ethics
Seeds: [old, old_woman, old_man]

Seeds ID: young-Rudinger_et_al_2017
Category: young
Used in Paper: Social Bias in Elicited Natural Language Inferences (Rudinger et al., 2017)
Source Categories: corpus-derived
Link: https://github.com/cjmay/snli-ethics
Seeds: [young, young_woman, young_man]

Seeds ID: race/ethnicity/nationality-Rudinger_et_al_2017
Category: race/ethnicity/nationality
Used in Paper: Social Bias in Elicited Natural Language Inferences (Rudinger et al., 2017)
Source Categories: corpus-derived
Link: https://github.com/cjmay/snli-ethics
Seeds: [indian, indian_woman, indian_man, asian, asians, asian_woman, asian_man, whie_woman, white_man, caucasian, american, american_woman, american_man, black_woman, black_man, native_american, african_american, african]

Seeds ID: white_collar_job-Fast_et_al_2016
Category: white_collar_job
Used in Paper: Empath: Understanding Topic Signals in Large-Scale Text (Fast et al., 2016)
Source Categories: crowd-sourced, curated, lexical-resources
Link: https://github.com/Ejhfast/empath-client
Seeds: [detective, executive, scientist, biologist, surgeon, vet, office, photographer, employer, colleague, psychiatrist, psychologist, qualified, wealthy, businesswoman, manager, therapist, attorney, forensics, lawyer, employment, workaholic, coroner, nurse, specialist, internship, job, neurologist, senator, promotion, retired, researcher, profession, engineer, accountant, entrepreneur, paperwork, counselling, successful, dentist, analyst, physician, hire, politician, consultant, retire, veterinarian, supervisor, examiner, inspector, doctor, actor, pharmacist, chemist, pediatrician, pediatric, director, professional, law, salary, chief, gynecologist]

Seeds ID: blue_collar_job-Fast_et_al_2016
Category: blue_collar_job
Used in Paper: Empath: Understanding Topic Signals in Large-Scale Text (Fast et al., 2016)
Source Categories: crowd-sourced, curated, lexical-resources
Link: https://github.com/Ejhfast/empath-client
Seeds: [serving, maid, employer, employee, salesperson, payday, pizzeria, clerk, supermarket, job, attendant, restaurant, waiter, waitress, worker, bartender, hostess, receptionist, cashier, paycheck, barista]

Seeds ID: domestic_work-Fast_et_al_2016
Category: domestic_work
Used in Paper: Empath: Understanding Topic Signals in Large-Scale Text (Fast et al., 2016)
Source Categories: crowd-sourced, curated, lexical-resources
Link: https://github.com/Ejhfast/empath-client
Seeds: [chore, mom, vacuum, scrubbing, cook, washing, baking, wash, morning, meal, house, chef, laundry, bake, organizing, cooking, spotless, mum, washer, remodeling, parent, job, nanny, kitchen, dishwasher, cleaning, family, cleaner, bathroom, errand, sitter, housekeeper, serve, housekeeping, tidy, cleaned, housework, scrub, organize, home, clean]

Seeds ID: occupation-Fast_et_al_2016
Category: occupation
Used in Paper: Empath: Understanding Topic Signals in Large-Scale Text (Fast et al., 2016)
Source Categories: crowd-sourced, curated, lexical-resources
Link: https://github.com/Ejhfast/empath-client
Seeds: [detective, producer, executive, manager, therapist, actor, electrician, occupation, retirement, office, photographer, maid, cashier, colleague, psychiatrist, bodyguard, psychologist, qualified, supervise, politician, surgeon, policeman, businesswoman, server, journalist, housekeeper, secretary, attorney, choreographer, chef, intern, lawyer, interpreter, employment, retire, nurse, officer, specialist, working, hairdresser, internship, clerk, job, nanny, waiter, pediatrician, pediatric, neurologist, senator, waitress, retired, profession, entrepreneur, florist, workplace, service, accountant, worker, singer, catering, dentist, technician, analyst, physician, hire, bartender, hostess, consultant, employ, veterinarian, caterer, entertainer, supervisor, publicist, agent, concierge, coordinator, receptionist, accounting, inspector, doctor, owner, assistant, interview, pharmacist, chemist, foreman, employee, qualification, workaholic, businessman, salary, baker, banker, gynecologist, professional, policewoman]

Seeds ID: attractive-Fast_et_al_2016
Category: attractive
Used in Paper: Empath: Understanding Topic Signals in Large-Scale Text (Fast et al., 2016)
Source Categories: crowd-sourced, curated, lexical-resources
Link: https://github.com/Ejhfast/empath-client
Seeds: [alluring, cute, attractively, athletic, desirable, breathtaking, perfect, swoon, sexiest, sassy, attractive, masculine, pleasing, captivating, fantastic, dreamy, charmingly, glamorous, seductive, mesmerizing, inviting, hunk, popular, fascinating, flatter, supermodel, fabulous, irresistible, enticing, appealing, dimpled, looking, attracted, adore, appeal, adorable, compliment, revealing, dashing, fantasize, stylish, sexy, flawless, tempting, envious, angelic, lovable, marvelous, hotter, blonde, charismatic, classically, hunky, dazzling, gorgeous, lovely, chiseled, pretty, impress, charming, feminine, handsome, toned, photogenic, admire, stunning, charmer, coolest, beautiful, provocative, beautifully, attract, dazzle, breathtakingly, physique, strikingly, hot, luscious, buff, beauty, attractiveness, fashionable, enchanting, curvy, built, tanned]

Seeds ID: ugliness-Fast_et_al_2016
Category: ugliness
Used in Paper: Empath: Understanding Topic Signals in Large-Scale Text (Fast et al., 2016)
Source Categories: crowd-sourced, curated, lexical-resources
Link: https://github.com/Ejhfast/empath-client
Seeds: [despise, balding, slimy, acne, grotesque, degrading, horrible, fat, diseased, repulsive, awful, nasty, brutish, grotesquely, distasteful, unworthy, scruffy, chubby, gross, insulting, crooked, revolting, unappealing, hairy, pathetic, cockroach, abnormally, unsightly, crippled, lousy, wrinkled, freakish, disfigured, disgusting, pudgy, tacky, obese, disgust, degrade, horrid, deformed, hideous, bloated, ugly, scum, demeaning, pig, obnoxious, blob, wart, disgraceful, fatty, bald, overweight, disgusted, unattractive, wrinkle, filthy, loathsome]

Seeds ID: masculine-Fast_et_al_2016
Category: masculine
Used in Paper: Empath: Understanding Topic Signals in Large-Scale Text (Fast et al., 2016)
Source Categories: crowd-sourced, curated, lexical-resources
Link: https://github.com/Ejhfast/empath-client
Seeds: [alluring, cockiness, attractively, athletic, cocky, aggressive, tattooed, jock, arrogance, masculine, hormone, dominate, males, overpowering, dreamy, brutish, stereotypical, guy, bulky, scruffy, authority, manly, baritone, hunky, lad, masculinity, hunk, appeal, surfer, strong, boy, testosterone, domineering, male, youthful, dude, fella, distinct, charisma, man, chiseled, puberty, mentality, boys, shouldered, handsome, rugged, intimidate, stature, figure, intimidating, muscular, brawny, beefy, attract, physique, athletically, biceps, attractiveness, stocky, hormonal, burly, egotistical, bodied, rowdy]

Seeds ID: feminine-Fast_et_al_2016
Category: feminine
Used in Paper: Empath: Understanding Topic Signals in Large-Scale Text (Fast et al., 2016)
Source Categories: crowd-sourced, curated, lexical-resources
Link: https://github.com/Ejhfast/empath-client
Seeds: [cute, gown, haircut, slimming, lacy, stunningly, curled, pretty, redhead, appealing, wearing, woman, wavy, silk, stylist, nicely, tights, finery, cleavage, stylish, brunette, lilac, elegant, supermodel, fabulous, girl, perfume, matching, blouse, silky, ruffled, purple, bikini, revealing, voluptuous, hairdresser, complement, makeup, sexy, dress, headband, blazer, layered, perky, clothes, pair, blonde, pantyhose, comb, jewelry, fuchsia, styling, accentuate, gorgeous, girls, impress, sophisticated, flowery, slinky, glam, wardrobe, glamorous, girlish, voluminous, stunning, beautiful, hairstyle, fashion, provocative, chic, skirt, curl, ballerina, fashionable, dressed, kimono, skater, frilly, halter, accessory, floral, jewelry, feminine, curve, curvy, lipstick, skinny]

Seeds ID: positive_emotion-Fast_et_al_2016
Category: positive_emotion
Used in Paper: Empath: Understanding Topic Signals in Large-Scale Text (Fast et al., 2016)
Source Categories: crowd-sourced, curated, lexical-resources
Link: https://github.com/Ejhfast/empath-client
Seeds: [happiness, enlighten, better, enthusiasm, pride, joyful, compassion, dearly, forgiving, kindness, bravery, closure, thrill, honestly, triumph, bond, honesty, alive, concern, reunite, joy, surprise, forgiveness, assurance, sympathize, understanding, reason, rejoice, care, faith, great, empathy, certainty, keep, trustworthy, affection, cherish, emotion, love, family, trusting, respect, trust, gratitude, confidence, adoration, friend, happy, overjoyed, determination, reassurance, glad, loved, admiration, wish, accomplishment, optimism, excitement, convince, hope, freedom, feeling, eagerness, willingness, sincere, sincerity, honest, genuine, comfort, elation, thrilled, loyalty, curiosity, unconditionally, proud]

Seeds ID: negative_emotion-Fast_et_al_2016
Category: negative_emotion
Used in Paper: Empath: Understanding Topic Signals in Large-Scale Text (Fast et al., 2016)
Source Categories: crowd-sourced, curated, lexical-resources
Link: https://github.com/Ejhfast/empath-client
Seeds: [violent, kill, hell, hate, dieing, death, thinking, hated, crying, surprised, hurting, worse, beat, stop, crushed, break, worst, trouble, disappointed, killed, lost, cry, worried, worst_part, bad, stupid, either, die, mean, insane, fucking, scared, hard, dead, beaten, horrible, monster, weak, loose, threatened, punch, killing, blame, reason, so_much_pain, hurts, losing, wanted, pissed, care, scary, accident, fault, guilty, terrible, swear, last_straw, heartbroken, scare, seeing, drunk, terrified, freaked, raped, frightened, poor_girl, lose, angry, fight, poor_guy, hurt, ashamed, depressed, unthinkable, tortured, crazy, confused, sad, hit, alone, lie, afraid, dying, shocked, angered, sick, badly, pain, react, wrong, mad, upset, fighting, furious]

Seeds ID: final_identities-Joseph_et_al_2017
Category: final identities
Used in Paper: Girls rule, boys drool: Extracting semantic and affective stereotypes on Twitter (Joseph et al, 2017)
Source Categories: corpus-derived, curated
Link: https://github.com/kennyjoseph/twitter_stereotype_extraction
Seeds: [detective, ambassador, coach, liar, sister, chinese, enemy, radical, stripper, bum, actress, russian, gf, manager, scientist, gunman, asian, victim, little, brother, mexican, prisoner, economist, mayor, principal, vet, instructor, police, buddy, candidate, feminist, photographer, father, police, officer, maid, indian, black, teenager, innocent, citizen, employee, speaker, supervisor, hater, colleague, woman, executive, teacher, jerk, civilian, ceo, assistant, advocate, coworker, patriot, vegan, arab, roommate, boxer, fan, mom, judge, politician, african, american, republican, lady, nurse, socialist, bro, toddler, scholar, pimp, artist, celebrity, sophomore, rapper, brother, clown, grandfather, spy, surgeon, priest, journalist, pilot, chick, guy, rich, grad, athlete, husband, secretary, user, prosecutor, attorney, nigga, female, japanese, democrat, chef, gangster, intern, individual, preacher, governor, lawyer, nephew, hero, girl, homeless, hacker, rapist, israeli, expert, announcer, rep, gentleman, conservative, grandmother, genius, activist, hipster, hooker, leader, juror, musician, bf, muslim, christian, bully, parent, reporter, lawmaker, white, man, middle, class, slut, boss, volunteer, bachelor, minor, rebel, grandma, worker, cousin, marine, loser, jew, cop, passenger, daughter, sheriff, survivor, canadian, thot, supporter, punk, host, millionaire, officer, soldier, american, taxpayer, murderer, mentor, neighbor, senator, virgin, protestor, voter, college, student, winner, family, deputy, blogger, singer, firefighter, intellectual, son, black, man, entrepreneur, follower, dancer, cheerleader, fool, liberal, blonde, employer, white, woman, engineer, killer, teammate, guest, sucker, dude, tourist, princess, inmate, runner, racist, editor, saint, mama, qb, man, pope, teen, commissioner, homosexual, champion, gay, boy, relative, farmer, pitcher, pastor, thief, poet, dentist, grandpa, adult, child, baby, niece, immigrant, junior, witness, boyfriend, customer, extremist, baptist, criminal, bartender, sibling, consultant, coward, believer, bride, hispanic, friend, lover, protester, daddy, dad, shooter, freak, angel, alcoholic, resident, pro, owner, player, critic, comedian, uncle, girlfriend, partner, nerd, hoe, native, catholic, bastard, author, idiot, veteran, producer, actor, hostage, patient, chairman, goon, moron, donor, asshole, latino, pal, rider, poor, thug, designer, minority, best, friend, stranger, professor, black, woman, vegetarian, terrorist, director, addict, student, writer, freshman, president, momma, spouse, lesbian, kid, geek, hypocrite, fighter, wife, atheist, doctor, white, academic, chief, client, aunt, mother, professional, guard, consumer, minister]

Seeds ID: verb_senses-Hoyle_et_al_2019
Category: verb senses
Used in Paper: Unsupervised Discovery of Gendered Language through Latent-Variable Modeling (Hoyle et al., 2019)
Source Categories:
Link:
Seeds: []

Seeds ID: male_singular-Hoyle_et_al_2019
Category: male singular
Used in Paper: Unsupervised Discovery of Gendered Language through Latent-Variable Modeling (Hoyle et al., 2019)
Source Categories: corpus-derived
Link:
Seeds: [man, boy, father, son, brother, husband, uncle, nephew, emperor, king, prince, duke, lord, knight, waiter, actor, god, policeman, postman, hero, wizard, steward, he]

Seeds ID: male_plural-Hoyle_et_al_2019
Category: male plural
Used in Paper: Unsupervised Discovery of Gendered Language through Latent-Variable Modeling (Hoyle et al., 2019)
Source Categories: corpus-derived
Link:
Seeds: [men, boys, fathers, sons, brothers, husbands, uncles, nephews, emperors, kings, princes, dukes, lords, knights, waiters, actors, gods, policemen, postmen, heros, wizards, stewards]

Seeds ID: female_singular-Hoyle_et_al_2019
Category: female singular
Used in Paper: Unsupervised Discovery of Gendered Language through Latent-Variable Modeling (Hoyle et al., 2019)
Source Categories: corpus-derived
Link:
Seeds: [woman, girl, mother, daughter, sister, wife, aunt, niece, empress, queen, princess, duchess, lady, dame, waitress, actress, goddess, policewoman, postwoman, heroine, witch, stewardess, she]

Seeds ID: female_plural-Hoyle_et_al_2019
Category: female plural
Used in Paper: Unsupervised Discovery of Gendered Language through Latent-Variable Modeling (Hoyle et al., 2019)
Source Categories: corpus-derived
Link:
Seeds: [women, girls, mothers, daughters, sisters, wives, aunts, nieces, empresses, queens, princesses, duchesses, ladies, dames, waitresses, actresses, goddesses, policewomen, postwomen, heroines, witches, stewardesses]

Seeds ID: personality_traits-Hoyle_et_al_2019
Category: personality traits
Used in Paper: Unsupervised Discovery of Gendered Language through Latent-Variable Modeling (Hoyle et al., 2019)
Source Categories: borrowed-from-social-sciences
Link:
Seeds: []

Seeds ID: feminine-Kaneko_and_Bollegala_2019
Category: feminine
Used in Paper: Gender-preserving Debiasing for Pre-trained Word Embeddings (Kaneko and Bollegala, 2019)
Source Categories: prior-work
Link: https://github.com/kanekomasahiro/gp_debias
Seeds: []

Seeds ID: masculine-Kaneko_and_Bollegala_2019
Category: masculine
Used in Paper: Gender-preserving Debiasing for Pre-trained Word Embeddings (Kaneko and Bollegala, 2019)
Source Categories: prior-work
Link: https://github.com/kanekomasahiro/gp_debias
Seeds: []

Seeds ID: gender-neutral-Kaneko_and_Bollegala_2019
Category: gender-neutral
Used in Paper: Gender-preserving Debiasing for Pre-trained Word Embeddings (Kaneko and Bollegala, 2019)
Source Categories: curated
Link: https://github.com/kanekomasahiro/gp_debias
Seeds: [abandonment, abate, aberrant, abiding, able, abolition, abomination, abrupt, absorbing, absorption, abstention, abstraction, absurd, absurdity, abundance, abundantly, accept, acceptable, access, accident, accidentally, accompany, accomplish, according, accordingly, account, accumulation, accurate, accuse, achieve, achievement, acid, acknowledge, acquire, actuality, adaptable, adaptation, addictive, adherence, adjacent, adjustable, adjustment, adjustments, admittedly, ado, adorable, adore, adorn, advancement, advent, adverse, adversity, advertisement, aerial, afar, affected, afternoon, ago, agonies, agree, agreement, agricultural, air, aircraft, airliner, airport, alarms, alongside, aloof, alternately, amazing, amazingly, amount, amusement, analogous, analyses, answer, apartments, apparatus, apparent, apparently, appetizer, apple, appoint, appointment, appreciate, approach, appropriate, approval, approve, approximately, argument, arms, arrests, art, ask, astray, attack, attempt, attention, attraction, attributes, authority, aware, back, backdrops, bad, balance, ballots, banana, bar, barely, barrel, base, battery, be, become, bed, before, beforehand, begin, beginning, behavior, behind, being, belief, believe, bell, belong, below, benches, benefits, best, better, big, bigger, biggest, billion, bit, bite, blackboard, blast, blizzard, blood, blouse, blow, blue, blues, board, boat, body, bomb, bombing, bond, bone, boon, bother, bottle, bottles, boxes, branch, brass, bread, breath, breeze, bright, brighter, building, bulletin, burn, burst, bus, buses, butter, bygone, calamity, calm, calmly, camera, camp, campaign, campus, can, cancer, candidate, canoe, canvas, cap, capability, capable, capacity, capital, capsule, captain, capture, car, carbon, card, cardboard, care, career, careful, carefully, carpet, carrier, carry, case, cash, cassette, cast, cataract, categorical, cathedral, cause, cell, center, centigrade, central, cerebellum, ceremonial, certain, certify, chair, chalk, chance, change, changing, channel, chapter, character, characteristic, characterize, charge, cheap, cheaper, cheapest, cheerful, cheerfully, chestnut, chin, china, choices, cinema, circuit, circulate, cities, city, clarify, clearance, clock, clumsy, coal, cocoon, coffee, coincide, cold, colder, color, come, comfort, comfortable, common, communicate, communication, community, compacts, company, comparison, competition, completion, compounds, condition, conference, confidence, confident, connection, consistent, control, conventional, conversation, coolest, copy, cork, corns, couch, cough, could, country, cover, crack, credit, creep, crime, crush, current, curve, daily, damage, damper, danger, dark, darkest, day, death, debate, debt, decade, decide, decided, decision, decrease, decreased, decreasing, deep, deeper, degree, denials, describe, described, describing, design, desire, destruction, detail, development, develops, different, digestion, direction, discover, discovering, discussion, disease, disgrace, disgust, disorder, display, dispute, distance, distant, distaste, distasteful, distinct, distinction, distinguish, distribute, distribution, district, diverse, diversity, divide, division, divorce, do, dodge, does, dollar, done, door, double, doubt, down, dream, dreams, dust, eagerly, ear, early, earn, earnings, earth, ease, easier, easily, east, eastern, easy, eat, eats, economic, edge, education, educations, effect, egg, end, enforcement, engage, english, enhance, enhanced, enhances, enhancing, equator, error, essentially, establish, establishment, estate, estimate, ethical, europe, evaporate, event, evidence, example, exchange, existence, expansion, experience, eye, eyelids, eyes, fact, fall, fast, faster, fear, fed, federal, fee, feed, feel, feeling, fellow, fiction, field, find, finds, finger, fire, flame, flashlight, flight, fly, flying, fold, food, force, form, free, freely, french, front, fruit, full, furniture, garbage, garlic, generate, generating, get, gets, give, glass, globe, go, goals, goes, going, gold, good, got, government, grain, grammar, grapefruit, grass, great, greater, greatest, green, grip, group, growth, half, hall, hand, handful, handle, hands, hang, hanger, happen, happily, happy, harbor, hard, harder, hardly, harmony, hate, have, head, heap, hear, hearing, heat, heavy, help, helps, hid, hidden, hill, history, hole, hope, hotel, hottest, hour, ice, idea, ideas, idiom, implement, importance, important, impose, impossible, impress, impression, impressions, impressive, improve, improvement, impulse, inconsistent, increase, increases, increasing, increasingly, incredible, indeed, industry, informative, ink, insect, insight, instinct, instrument, insurance, interest, international, invention, iron, is, issues, its, jar, join, jump, junction, keenly, keep, kept, killing, kit, knew, know, knowledge, known, label, ladders, land, language, largest, late, latent, laugh, lead, learning, leather, leave, leaves, leg, lemon, length, let, letter, level, lift, light, like, likes, limit, liquid, list, listen, listened, listening, listens, lists, live, load, local, london, long, longer, longest, look, looked, looking, loss, lounge, low, lower, lows, luck, luckiest, lucky, main, mainly, mains, mainstream, maintain, maintenance, major, majority, make, maker, many, margin, mark, may, meal, mean, measure, meat, meeting, memory, metal, middle, might, millionth, mind, minority, minute, miracle, mist, money, month, morning, motion, mountain, mouth, move, moved, mucus, multiple, museum, music, myriad, name, narrower, nation, national, necessarily, necessary, neck, need, net, new, newer, newly, news, nice, night, nightly, noise, noisier, noisiest, none, nonetheless, nor, normal, normally, nose, note, number, oases, observation, offer, oil, old, older, oldest, onion, onions, only, operation, opinion, order, organization, other, overflow, page, pain, paint, paper, part, particular, paste, payment, pen, pencil, pending, pepper, perfect, perfectly, perform, phenomena, phone, photo, pitches, place, play, pleasure, plenty, point, poison, pole, polish, pool, porter, position, possible, powder, power, predicts, price, print, problems, process, produce, productive, profit, property, prose, protest, pull, punishment, pupils, purpose, push, put, quality, question, quick, quicker, quickly, quieting, rain, rainfalls, range, rank, rapid, rapidly, rare, rarely, rate, ratios, raw, ray, reach, react, reaction, reading, real, reason, recent, recliner, recognize, recommend, recommendation, record, reflect, reflection, regret, relation, religion, remind, rent, reportedly, representative, request, resemblance, respect, rest, result, retain, reward, rhythm, rhythms, rice, right, river, road, roll, roof, room, rub, rule, run, running, safe, safely, safer, salt, sand, saw, say, says, scale, schedule, scheme, screamed, screaming, sea, seat, second, secretary, see, seeing, seem, seemingly, sees, selection, self, sense, sentence, serious, seriously, servant, seventh, several, shade, shake, shelf, shirt, shock, shoes, should, show, shuffle, shuffles, sick, side, siege, sign, silk, silver, simplest, size, skill, sky, sleep, sleeping, slept, slip, slope, slow, slower, slowing, slowly, smarter, smartest, smash, smell, smile, smoke, smoothing, sneeze, snow, society, some, song, sort, sound, space, speak, speaks, special, spending, stacks, stage, stanford, start, statement, steam, steel, step, stitch, stone, stones, stop, story, stress, stretch, stronger, structure, substance, sufficiently, sugar, suggestion, summer, support, surprise, swift, swiftly, swim, system, systems, take, talk, taste, tasteful, tax, teeth, tell, ten, tendency, test, text, then, theories, theory, thing, things, think, thinks, thought, thousand, thousandth, thump, thunder, time, tin, tissue, title, tokyo, tomatoes, tongue, took, top, touch, tougher, town, toy, trade, transfer, transport, tree, trick, trouble, try, turn, twist, unacceptable, unaware, uncomfortable, undecided, underside, unexpectedly, unfortunately, unimpressive, uninformative, unit, university, unknown, unproductive, use, useful, utterly, value, vanishes, variously, verse, very, vessel, view, visibility, visit, voice, walk, want, ware, warmer, was, wash, waste, water, watery, wave, way, weakest, weather, week, weekend, weeks, welfare, went, westward, whichever, whole, widest, will, wind, wine, winter, wire, wishes, wood, wool, word, words, world, worse, worst, would, wound, writing, yacht, year, yearly, yen, young, younger, youngest, zero, zigzag, zone]

Seeds ID: stereotypeical-Kaneko_and_Bollegala_2019
Category: stereotypeical
Used in Paper: Gender-preserving Debiasing for Pre-trained Word Embeddings (Kaneko and Bollegala, 2019)
Source Categories: prior-work
Link: https://github.com/kanekomasahiro/gp_debias
Seeds: []

Seeds ID: male_names-Knoche_et_al_2019
Category: male names
Used in Paper: Identifying Biases in Politically Biased Wikis through Word Embeddings (Knoche et al., 2019)
Source Categories: prior-work
Link: https://github.com/MKnoche/wiki_bias_embedding
Seeds: [john, paul, mike, kevin, steve, greg, jeff, bill]

Seeds ID: female_names-Knoche_et_al_2019
Category: female names
Used in Paper: Identifying Biases in Politically Biased Wikis through Word Embeddings (Knoche et al., 2019)
Source Categories: borrowed-from-social-sciences, prior-work
Link: https://github.com/MKnoche/wiki_bias_embedding
Seeds: [amy, joan, lisa, sarah, diana, kate, ann, donna]

Seeds ID: male_terms-Knoche_et_al_2019
Category: male terms
Used in Paper: Identifying Biases in Politically Biased Wikis through Word Embeddings (Knoche et al., 2019)
Source Categories: borrowed-from-social-sciences, prior-work
Link: https://github.com/MKnoche/wiki_bias_embedding
Seeds: [male, man, boy, brother, he, him, his, son, father, uncle, grandfather]

Seeds ID: female_terms-Knoche_et_al_2019
Category: female terms
Used in Paper: Identifying Biases in Politically Biased Wikis through Word Embeddings (Knoche et al., 2019)
Source Categories: borrowed-from-social-sciences, prior-work
Link: https://github.com/MKnoche/wiki_bias_embedding
Seeds: [female, woman, girl, sister, she, her, hers, daughter, mother, aunt, grandmother]

Seeds ID: male-Knoche_et_al_2019
Category: male
Used in Paper: Identifying Biases in Politically Biased Wikis through Word Embeddings (Knoche et al., 2019)
Source Categories: borrowed-from-social-sciences, prior-work
Link: https://github.com/MKnoche/wiki_bias_embedding
Seeds: [john, paul, mike, kevin, steve, greg, jeff, bill, male, man, boy, brother, he, him, his, son, father, uncle, grandfather]

Seeds ID: female-Knoche_et_al_2019
Category: female
Used in Paper: Identifying Biases in Politically Biased Wikis through Word Embeddings (Knoche et al., 2019)
Source Categories: borrowed-from-social-sciences, prior-work
Link: https://github.com/MKnoche/wiki_bias_embedding
Seeds: [amy, joan, lisa, sarah, diana, kate, ann, donna, female, woman, girl, sister, she, her, hers, daughter, mother, aunt, grandmother]

Seeds ID: white_names-Knoche_et_al_2019
Category: white names
Used in Paper: Identifying Biases in Politically Biased Wikis through Word Embeddings (Knoche et al., 2019)
Source Categories: borrowed-from-social-sciences, prior-work
Link: https://github.com/MKnoche/wiki_bias_embedding
Seeds: [adam, chip, harry, josh, roger, alan, frank, ian, justin, ryan, andrew, fred, jack, matthew, stephen, brad, greg, jed, paul, todd, brandon, hank, jonathan, peter, wilbur, amanda, courtney, heather, melanie, sara, amber, crystal, katie, meredith, shannon, betsy, donna, kristin, nancy, stephanie, bobbie-sue, ellen, lauren, peggy, sue-ellen, colleen, emily, megan, rachel, wendy, brendan, geoffrey, brett, jay, neil, anne, carrie, jill, laurie, kristen, sarah]

Seeds ID: black_names-Knoche_et_al_2019
Category: black names
Used in Paper: Identifying Biases in Politically Biased Wikis through Word Embeddings (Knoche et al., 2019)
Source Categories: borrowed-from-social-sciences, prior-work
Link: https://github.com/MKnoche/wiki_bias_embedding
Seeds: [alonzo, jamel, lerone, percell, theo, alphonse, jerome, leroy, rasaan, torrance, darnell, lamar, lionel, rashaun, tyree, deion, lamont, malik, terrence, tyrone, everol, lavon, marcellus, terryl, wardell, aiesha, lashelle, nichelle, shereen, temeka, ebony, latisha, shaniqua, tameisha, teretha, jasmine, latonya, shanise, tanisha, tia, lakisha, latoya, sharise, tashika, yolanda, lashandra, malika, shavonn, tawanda, yvette, hakim, jermaine, kareem, jamal, rasheed, aisha, keisha, kenya, tamika]

Seeds ID: christianity_words-Knoche_et_al_2019
Category: christianity words
Used in Paper: Identifying Biases in Politically Biased Wikis through Word Embeddings (Knoche et al., 2019)
Source Categories: borrowed-from-social-sciences, prior-work
Link: https://github.com/MKnoche/wiki_bias_embedding
Seeds: [baptism, messiah, catholicism, resurrection, christianity, salvation, protestant, gospel, trinity, jesus, christ, christian, cross, catholic, church]

Seeds ID: islam_words-Knoche_et_al_2019
Category: islam words
Used in Paper: Identifying Biases in Politically Biased Wikis through Word Embeddings (Knoche et al., 2019)
Source Categories: borrowed-from-social-sciences, prior-work
Link: https://github.com/MKnoche/wiki_bias_embedding
Seeds: [allah, ramadan, turban, emir, salaam, sunni, koran, imam, sultan, prophet, veil, ayatollah, shiite, mosque, islam, sheik, muslim, muhammad]

Seeds ID: atheism_words-Knoche_et_al_2019
Category: atheism words
Used in Paper: Identifying Biases in Politically Biased Wikis through Word Embeddings (Knoche et al., 2019)
Source Categories: borrowed-from-social-sciences, prior-work
Link: https://github.com/MKnoche/wiki_bias_embedding
Seeds: [atheism, atheist, atheistic, heliocentric, evolution, darwin, galilei, agnostic, agnosticism, pagan, science, disbelief, scepticism, philosophy, university, kopernikus]

Seeds ID: pleasant-Knoche_et_al_2019
Category: pleasant
Used in Paper: Identifying Biases in Politically Biased Wikis through Word Embeddings (Knoche et al., 2019)
Source Categories: borrowed-from-social-sciences, prior-work
Link: https://github.com/MKnoche/wiki_bias_embedding
Seeds: [caress, freedom, health, love, peace, cheer, friend, heaven, loyal, pleasure, diamond, gentle, honest, lucky, rainbow, diploma, gift, honor, miracle, sunrise, family, happy, laughter, paradise, vacation, joy, wonderful]

Seeds ID: unpleasant-Knoche_et_al_2019
Category: unpleasant
Used in Paper: Identifying Biases in Politically Biased Wikis through Word Embeddings (Knoche et al., 2019)
Source Categories: borrowed-from-social-sciences, prior-work
Link: https://github.com/MKnoche/wiki_bias_embedding
Seeds: [abuse, crash, filth, murder, sickness, accident, death, grief, poison, stink, assault, disaster, hatred, pollute, tragedy, divorce, jail, poverty, ugly, cancer, kill, rotten, vomit, agony, prison, terrible, horrible, nasty, evil, war, awful, failure]

Seeds ID: science-Knoche_et_al_2019
Category: science
Used in Paper: Identifying Biases in Politically Biased Wikis through Word Embeddings (Knoche et al., 2019)
Source Categories: borrowed-from-social-sciences, prior-work
Link: https://github.com/MKnoche/wiki_bias_embedding
Seeds: [math, algebra, geometry, calculus, equations, computation, numbers, addition, science, technology, physics, chemistry, einstein, nasa, experiment, astronomy]

Seeds ID: art-Knoche_et_al_2019
Category: art
Used in Paper: Identifying Biases in Politically Biased Wikis through Word Embeddings (Knoche et al., 2019)
Source Categories: borrowed-from-social-sciences, prior-work
Link: https://github.com/MKnoche/wiki_bias_embedding
Seeds: [poetry, art, dance, literature, novel, symphony, drama, sculpture, shakespeare]

Seeds ID: intellectual_words-Knoche_et_al_2019
Category: intellectual words
Used in Paper: Identifying Biases in Politically Biased Wikis through Word Embeddings (Knoche et al., 2019)
Source Categories: borrowed-from-social-sciences, prior-work
Link: https://github.com/MKnoche/wiki_bias_embedding
Seeds: [precocious, resourceful, inquisitive, sagacious, inventive, astute, adaptable, reflective, discerning, intuitive, inquiring, judicious, analytical, luminous, venerable, imaginative, shrewd, thoughtful, sage, smart, ingenious, clever, brilliant, logical, intelligent, apt, genius, wise, stupid, dumb, dull, clumsy, foolish, naive, unintelligent, trivial, unwise, idiotic]

Seeds ID: appearance_words-Knoche_et_al_2019
Category: appearance words
Used in Paper: Identifying Biases in Politically Biased Wikis through Word Embeddings (Knoche et al., 2019)
Source Categories: borrowed-from-social-sciences, prior-work
Link: https://github.com/MKnoche/wiki_bias_embedding
Seeds: [alluring, voluptuous, blushing, homely, plump, sensual, gorgeous, slim, bald, athletic, fashionable, stout, ugly, muscular, slender, feeble, handsome, healthy, attractive, fat, weak, thin, pretty, beautiful, strong]

Seeds ID: career-Knoche_et_al_2019
Category: career
Used in Paper: Identifying Biases in Politically Biased Wikis through Word Embeddings (Knoche et al., 2019)
Source Categories: borrowed-from-social-sciences, prior-work
Link: https://github.com/MKnoche/wiki_bias_embedding
Seeds: [executive, management, professional, corporation, salary, office, business, career]

Seeds ID: family-Knoche_et_al_2019
Category: family
Used in Paper: Identifying Biases in Politically Biased Wikis through Word Embeddings (Knoche et al., 2019)
Source Categories: borrowed-from-social-sciences, prior-work
Link: https://github.com/MKnoche/wiki_bias_embedding
Seeds: [home, parents, children, family, cousins, marriage, wedding, relatives]

Seeds ID: high_morality_and_low/neutral_warmth-Bhatia_et_al_2018
Category: high morality and low/neutral warmth
Used in Paper: Trait associations for Hillary Clinton and Donald Trump in news media: A computational analysis (Bhatia et al., 2018)
Source Categories: borrowed-from-social-sciences
Link:
Seeds: [courageous, fair, principled, responsible, just, honest, trustworthy, loyal]

Seeds ID: low/neutral_and_morality_high_warmth-Bhatia_et_al_2018
Category: low/neutral and morality high warmth
Used in Paper: Trait associations for Hillary Clinton and Donald Trump in news media: A computational analysis (Bhatia et al., 2018)
Source Categories: borrowed-from-social-sciences
Link:
Seeds: [warm, sociable, happy, agreeable, enthusiastic, easygoing, funny, playful]

Seeds ID: high_competence-Bhatia_et_al_2018
Category: high competence
Used in Paper: Trait associations for Hillary Clinton and Donald Trump in news media: A computational analysis (Bhatia et al., 2018)
Source Categories: borrowed-from-social-sciences
Link:
Seeds: [athletic, musical, creative, innovative, intelligent, organized, logical, clever]

Seeds ID: male_words_Penn_Treebank-Bordia_and_Bowman_2019
Category: male words (Penn Treebank)
Used in Paper: Identifying and Reducing Gender Bias in Word-Level Language Models (Bordia and Bowman, 2019)
Source Categories: curated
Link:
Seeds: [actor, boy, father, he, him, his, male, man, men, son, sons, spokesman, wife, king, brother]

Seeds ID: female_words_Penn_Treebank-Bordia_and_Bowman_2019
Category: female words (Penn Treebank)
Used in Paper: Identifying and Reducing Gender Bias in Word-Level Language Models (Bordia and Bowman, 2019)
Source Categories: curated
Link:
Seeds: [actress, girl, mother, she, her, her, female, woman, women, daughter, daughters, spokeswoman, husband, queen, sister]

Seeds ID: male_words_WikiText_2-Bordia_and_Bowman_2019
Category: male words (WikiText-2)
Used in Paper: Identifying and Reducing Gender Bias in Word-Level Language Models (Bordia and Bowman, 2019)
Source Categories: curated
Link:
Seeds: [actor, Actor, boy, Boy, boyfriend, Boys, boys, father, Father, Fathers, fathers, Gentleman, gentleman, gentlemen, Gentlemen, grandson, he, He, hero, him, Him, his, His, Husband, husbands, King, kings, Kings, male, Male, males, Males, man, Man, men, Men, Mr., Prince, prince, son, sons, spokesman, stepfather, uncle, wife, king]

Seeds ID: female_words_WikiText_2-Bordia_and_Bowman_2019-Bordia_and_Bowman_2019
Category: female words WikiText-3
Used in Paper: Identifying and Reducing Gender Bias in Word-Level Language Models (Bordia and Bowman, 2019)
Source Categories: curated
Link:
Seeds: [actress, Actress, girl, Girl, girlfriend, Girls, girls, mother, Mother, Mothers, mothers, Lady, lady, ladies, Ladies, granddaughter, she, She, heroine, her, Her, her, Her, Wife, wives, Queen, queens, Queens, female, Female, females, Females, woman, Woman, women, Women, Mrs., Princess, princess, daughter, daughters, spokeswoman, stepmother, aunt, husband, queen]

Seeds ID: male_words_CNN_DailyMail-Bordia_and_Bowman_2019
Category: male words (CNN/Daily Mail)
Used in Paper: Identifying and Reducing Gender Bias in Word-Level Language Models (Bordia and Bowman, 2019)
Source Categories: curated
Link:
Seeds: [actor, boy, boyfriend, boys, father, fathers, gentleman, gentlemen, grandson, he, him, his, husbands, kings, male, males, man, men, prince, son, sons, spokesman, stepfather, uncle, wife, king, brother, brothers]

Seeds ID: female_words_CNN_DailyMail-Bordia_and_Bowman_2019
Category: female words (CNN/Daily Mail)
Used in Paper: Identifying and Reducing Gender Bias in Word-Level Language Models (Bordia and Bowman, 2019)
Source Categories: curated
Link:
Seeds: [actress, girl, girlfriend, girls, mother, mothers, lady, ladies, granddaughter, she, her, her, wives, queens, female, females, woman, women, princess, daughter, daughters, spokeswoman, stepmother, aunt, husband, queen, sister, sisters]

Seeds ID: words_to_debias-Kumar_et_al_2020
Category: words to debias
Used in Paper: Nurse is Closer to Woman than Surgeon? Mitigating Gender-BiasedProximities in Word Embeddings (Kumar et al., 2020)
Source Categories: prior-work
Link: https://github.com/TimeTraveller-San/RAN-Debias
Seeds: []

Seeds ID: male_identity-Park_et_al_2018
Category: common gender identity pairs (male)
Used in Paper: Reducing Gender Bias in Abusive Language Detection (Park et al., 2018)
Source Categories: unknown
Link:
Seeds: []

Seeds ID: female_identity-Park_et_al_2018
Category: common gender identity pairs (female)
Used in Paper: Reducing Gender Bias in Abusive Language Detection (Park et al., 2018)
Source Categories: unknown
Link:
Seeds: []

Seeds ID: neutral-Park_et_al_2018
Category: neutral nouns and adjectives
Used in Paper: Reducing Gender Bias in Abusive Language Detection (Park et al., 2018)
Source Categories: unknown
Link:
Seeds: []

Seeds ID: offensive-Park_et_al_2018
Category: offensive nouns and adjectives
Used in Paper: Reducing Gender Bias in Abusive Language Detection (Park et al., 2018)
Source Categories: unknown
Link:
Seeds: []