We appended POS tags to every word using Oliver Mason's Qtag program [<A
HREF="node20.html#qtag">6</A>]. This serves as a rough way to disambiguate words that may hold different meanings in different contexts. For example, it would distinguish the different uses of “love” in ``I love this movie'' versus ``This is a love story.'' However, it turns out that word disambiguation is a much more complicated problem, as POS says nothing to distinguish between the meaning of cold in ``I was a bit cold during the movie'' and ``The cold murderer chilled my heart.''
Part of speech tagging was not very helpful for unigram results; in fact, the NB classifier did slightly worse with parts of speech tagged when using unigrams. However, when using bigrams, the MaxEnt and SVM classifiers did significantly better, achieving 3-4% better accuracy with part of speech tagging when measuring frequency and presence information.