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************************************************ Hidden Markov Models for POS tagging Data: twitter data with POS tags Smoothing method: linear interpolation Decoding: Viterbi author : An Yan date: Feb, 2017 Python version: 2.7 ************************************************* HOW TO RUN: example: python yanan_hmm.py -t 5 twt.train.json twt.dev.json ** this will output accuracy/confusion matrix of both ** bigram and trigram hmm on dev data with pre-trained ** best lamda.lamda =(0.001, 0.999) for bigram_hmm and ** lamda = (0.6, 0.3, 0.1) for trigram hmm **************************** parameters: -t: unk threshold, default = 1. test data set dev data set or test data set. ***************************** Help message, please type python yanan_lm.py -h ************************************************ note: if you run "python yanan_hmm.py -t 5 twt.train.json twt.dev.json", you will get accuracies of bigram and trigram hmm on dev data only. ****************************************************************
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Use HMM to do POS tagging. Viterbi decoding and linear interpolation smoothing
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