Natural language processing (NLP) is an important research area in artificial intelligence, dating back to at least the 1950’s. One of the most basic problems in NLP is part-of-speech tagging, in which the goal is to mark every word in a sentence with its part of speech (noun, verb, adjective, etc.). This is a first step towards extracting semantics from natural language text.

Data: The dataset is a large corpus of labeled training and testing data, consisting of nearly 1 million words and 50,000 sentences. The file format of the datasets is: each line consists of a word, followed by a space, followed by one of 12 part-of-speech tags: ADJ (adjective), ADV (adverb), ADP (adposition), CONJ (conjunction), DET (determiner), NOUN, NUM (number), PRON (pronoun), PRT (particle), VERB, X (foreign word), and . (punctuation mark). Sentence boundaries are indicated by blank lines.

label.py is the main program, pos scorer.py, which has the scoring code, and pos solver.py, which contains the actual part-of-speech estimation code. The program takes as input two filenames: a training file and a testing file and displays accuracy using simple probability, Bayes net variable elimination method and Viterbi algorithm to find the maximum a posteriori (MAP).

It also displays the logarithm of the posterior probability for each solution it finds, as well as a running evaluation showing the percentage of words and whole sentences that have been labeled correctly according to the ground truth.

To run the code:
python label.py part2 training_file testing_file