Hidden Markov Models for Parts of speech tagging in Python
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Updated
Mar 22, 2017 - Python
Hidden Markov Models for Parts of speech tagging in Python
A toy pos tagger applied Hidden Markov Model.
This repo contains the python implementation of the Forward algo and Viterbi algo, which are used in HMM i.e. Hidden Markov Model, in NLP (Natural Language Processing)
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Hidden Markov Models (HMMs) for estimating the sequence of hidden states (decoding) via the Viterbi algorithm, and estimating model parameters (learning) via the Baum- Welch algorithm.
Train a first-order (i.e., the probability of a tag depends only on the previous tag) HMM part-of-speech tagger. Find the MAP estimate of the parameters of the model using add-1 smoothing.
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Uses Viterbi algorithm to classify text with their respective parts of speech tags. Consist of a learning module that calculates transition and emission probabilities of the training set and applies this model on the test data set. Unknown words of the test are given a fixed probability.
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Introduction to Digital Speech Processing (DSP), 2021 Autumn, LS Lee.
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