Author: Md Kamrul Hasan Email: mhasan8@cs.rochester.edu 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.
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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.
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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)
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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.