This is a bigram Hidden Markov Model part of speech tagger that uses the Viterbi algorithm for decoding. The model is trained on the Wall Street Journal POS corpus and attempts to handle unknown words by performing suffix analysis using suffix trees as described by (Brants, 2000).

Trained POS tagger

The code for that application can be found here.

This was primarily tested on Java 11.

To run from the command line, make sure you are in the root directory viterbi. This is the directory that contains src and WSJ_POS_CORPUS_FOR_STUDENTS. Run

javac src/viterbi/*.java

Then to train and evaluate

java -cp src viterbi.WSJPOSTagger WSJ_POS_CORPUS_FOR_STUDENTS/WSJ_02-21.pos TEST_FILE MAX_SUFFIX_LENGTH MAX_WORD_FREQUENCY

where TEST_FILE is the file with sentences that you want to tag, MAX_SUFFIX_LENGTH is the maximum suffix length to use for the suffix tree and MAX_WORD_FREQUENCY is the maximum word frequency as found in the training set of the words to use for the suffix tree.

Note: The sentences in the test file must have the same format as the sentences in WSJ_POS_CORPUS_FOR_STUDENTS/WSJ_23.words.

Brants, T. (2000). TnT: A statistical part-of-speech tagger. In ANLP 2000, Seattle, WA, pp. 224–231.