Hidden Markov Model Named Entity Tagger

Train a tagger on a training data and predict tags for input words by using HMM and Viterbi decoding.

Usage: python hmm_ne_tagger.py [options] [command] [counts_file] < [input_file]

Command: trigram prints the log probabilities for the input_file decode decodes the tag sequences for the input_file with the log probabilities

Options: -h, --help show this help message and exit -d, --digits use DIGITS symbol for the words which are compounded only digits -i, --init-capital use INIT_CAPITAL symbol for the words start with uppercase character -a, --all-capital use ALL_CAPITAL symbol for the all capitalized words