Topics

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We followed Speech and Language Processing, 3rd Ed., as the primary text book for the course.

1. Regular Expressions, Text Normalization, Edit Distance

2. Edit Distance

3. N-gram Language Models

4. Naive Bayes and Sentiment Classification

5. Logistic Regression

6. Vector Semantics and Embeddings

7. Neural Networks and Neural Language Models

8. Sequence Labeling for Parts of Speech and Named Entities

9. Deep Learning Architectures for Sequence Processing

10. Contextual Embeddings

11. Machine Translation

12. Constituency Grammars

13. Constituency Parsing

14. Dependency Parsing

15. Logical Representations of Sentence Meaning

16. Computational Semantics and Semantic Parsing

17. Information Extraction

18. Word Senses and WordNet

19. Semantic Role Labeling and Argument Structure

20. Lexicons for Sentiment, Affect, and Connotation 2

21. Coreference Resolution

22. Discourse Coherence

23. Question Answering

24. Chatbots and Dialogue Systems

25. Phonetics

26. Automatic Speech Recognition and Text-to-Speech

Workshops Included with The Course

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• Working with Data (Regular Expressions) https://librarycarpentry.org/lc-data-intro/

• The Unix Shell http://swcarpentry.github.io/shell-novice

• Version Control with Git http://swcarpentry.github.io/git-novice

• Programming with Python http://swcarpentry.github.io/python-novice-inflammation

• Plotting and Programming in Python http://swcarpentry.github.io/python-novice-gapminder

• Text & Data Mining http://librarycarpentry.org/lc-tdm Constellate - https://ithaka.github.io/tdm-notebooks/book/all-notebooks.html HathiTrust Research Center Tools & Data - https://wiki.htrc.illinois.edu/display/COM/Getting+started

• Introduction to web scraping https://ubc-library-rc.github.io/intro-web-scraping/ https://librarycarpentry.org/lc-webscraping

• Programming with R http://swcarpentry.github.io/r-novice-inflammation

• R for Reproducible Scientific Analysis http://swcarpentry.github.io/r-novice-gapminder

• Intro to Conda for Data Scientists https://carpentries-incubator.github.io/introduction-to-conda-for-data-scientists/

• Text Processing and Parallel Processing https://deapsecure.gitlab.io/deapsecure-lesson01-hpc/

• Parallel Programming in Python https://carpentries-incubator.github.io/lesson-parallel-python/

• Intro to AI for GLAM https://carpentries-incubator.github.io/machine-learning-librarians-archivists/

• Introduction to Machine Learning with Scikit Learn https://carpentries-incubator.github.io/machine-learning-novice-sklearn/

• Introduction to deep-learning https://carpentries-incubator.github.io/deep-learning-intro/index.html

• Logistic regression for public health https://carpentries-incubator.github.io/logistic-regression-public-health/index.html

• Simple linear regression for public health https://carpentries-incubator.github.io/simple-linear-regression-public-health/

• Multiple linear regression for public health https://carpentries-incubator.github.io/multiple-linear-regression-public-health/

• Text Analysis in Python https://carpentries-incubator.github.io/python-text-analysis/

Acknowledgements

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Heartiest gratitude to:

Annajiat Alim Rasel, Senior Lecturer, Department of Computer Science and Engineering and Deputy Director, Institutional Quality Assurance Cell, BRAC University. Also the instructor of this course.

Dan Jurafsky, Jackson Eli Reynolds Professor in Humanities, Professor of Linguistics, Professor of Computer Science, Stanford University

and his co-author,

James H. Martin, Professor of Computer Science and a fellow in the Institute of Cognitive Science, University of Colorado Boulder for their open source book, Speech and Language Processing, 3rd Ed. which was the primary learning textbook.

Software Carpentry, for their open source NLP learning repos.