-> BiGrams
Compute the Bigram Model(counts and probabilities) on a given corpus for following three scenarios:
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No Smoothing
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Add-one Smoothing
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Good-Turing Discounting based Smoothing
Run Command: python BiGrams.py <path of the file CorpusTreebank2.txt>
-> Brills
Implement Brill’s transformation-based POS tagging algorithm using ONLY the previous word’s tag to extract the best transformation rule to:
i. Transform “NN” to “JJ”
ii. Transform “NN” to “VB”
Using the learnt rules, fill out the missing POS tags (for the words “standard” and “work”) in the following sentence:
The_DT standard_?? Turbo_NN engine_NN is_VBZ hard_JJ to_TO work_??
Run Command: python Brills.py <path of the file POSTaggedTrainingSet.txt>
-> POS Probabilities
Compute the bigram models (counts and probabilities) required by Naïve Bayesian Classification (Bigram) based POS Tagging.
Run Command: python POS_probs.py <path of the file POSTaggedTrainingSet.txt>
-> ViterbiHMM
The Viterbi algorithm to compute the most likely weather sequence and probability for any given observation sequence. Example observation sequences: 331, 122313, 331123312, etc.
The Given HMM:
Run Command: python ViterbiHMM.py