Skip to content

matalvepu/Perceptron-POS-Tagging

Folders and files

NameName
Last commit message
Last commit date

Latest commit

 

History

4 Commits
 
 
 
 
 
 
 
 
 
 

Repository files navigation

Perceptron-for-POS-Tagging

Author: Md Kamrul Hasan Email: [email protected] Date: 9/15/2017

=============================================================================================== Description: Implementation of Discriminative training approach (Perceptron) for POS tagging

I did it as a part of homework problem in the Statistical Speech and Language Processing class taught by Prof Daniel Gildea (https://www.cs.rochester.edu/~gildea/) in Fall 2017.

=============================================================================================== Instruction to run:

python perceptron_final.py train test

You can change number of iteration in perceptron method by setting n_itr=n.

===============================================================================================

Preprocessing: I have preprocessed all training at the beginning to generate all features and associated weights. As a feature, I only emissions and transitions. Then I run perceptron using the viterbi. For every single training instances, I updated the weights acccording to the errors that were genarted by viterbi sequence.

===============================================================================================

Testing:

Perceptron return the updated weight. Using that weight and viterbi, I have calculated average accuarcy for the test set.

Accuracy: 91.75 (After four iteration)

Accuray: 90.2 % (Single iteration)

Accuracy: 91.09 % (Two iterations)

===============================================================================================

In homewrok of HMM Decoder, I got the accuray around 94% where I used smoothing techniques that improved my accuracy. I think if I add other features like Start , end states, caps for the start word it will improve the accuray more.

About

No description, website, or topics provided.

Resources

Stars

Watchers

Forks

Releases

No releases published

Packages

No packages published

Languages