Taggers for Parsers (1996)
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@MISC{Charniak96taggersfor,
author = {Eugene Charniak and John Adcock and Jeremy Katz and Glenn Carroll and Glenn Carroll and Anthony Cassandra and Anthony Cassandra and Yoshihiko Gotoh and Yoshihiko Gotoh and Michael Littman and Michael Littman and John Mccann and John Mccann},
title = {Taggers for Parsers},
year = {1996}
}
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Abstract
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. 1 Introduction Recent years have seen a spate of research on various techniques for "tagging " --- assigning a part of speech to each word in a text [1,2,5,6,8,9,10,12, 13]. One justification for such research is that a tagger can serve as a front end to a parser: the tagger assigns the tags to t...
