Tech Report CS-94-06
Taggers for Parsers
Eugene Charniak, Glenn Carroll, John Adcock, Anthony Cassandra, Yoshihiko Gotoh, Jeremy Katz, Michael Littman, and John McCann
We consider what tagging models are most appropriate as front ends for probabilistic context-free-grammar parsers. In particular we ask if using a tagger that returns more than one tag, a ``multple tagger,'' improves parsing performance. Our conclusion is somewhat surprising: single tag Markov-model taggers are quite adequate for the task. First of all, parsing accuracy, as measured by the correct assignment of parts of speech to words, does not increase significantly when parsers select the tags themselves. In addition, the work required to parse a sentence goes up with increasing tag ambiguity, though not as much as one might expect. Thus, for the moment, single taggers are the best taggers.