An Experiment In Semantic Tagging Using Hidden Markov Model Tagging

The same word can have many different meanings depending on the context in which it is used. Discovering the meaning of a word, given the text around it, has been an interesting problem for both the psychology and the artificial intelligence research communities. In this article, we present a series of experiments, using methods which have proven to be useful for eliminating part-of-speech ambiguity, to see if such simple methods can be used to resolve semantic ambiguities. Using a publicly available semantic lexicon, we find the Hidden Markov Models work surprising well at choosing the right semantic categories, once the sentence has been stripped of purely functional words.