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Evaluating evolutionary algorithms

✍ Scribed by W. Whitney; S. Rana; J. Dzubera; K.E. Mathias

Publisher: Elsevier Science
Year: 1996
Tongue: English
Weight: 165 KB
Volume: 84
Category: Article
ISSN: 0004-3702
DOI: 10.1016/0004-3702(96)81371-3

No coin nor oath required. For personal study only.

✦ Synopsis

We consider what tagging models are most appropriate as front ends for probabilistic context-free grammar parsers. In particular, we ask if using a "multiple tagger", a tagger that returns more than one tag, 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.

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