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Lexical Adaptation of Link Grammar to the Biomedical Sublanguage: a Comparative Evaluation of Three Approaches
We study the adaptation of Link Grammar Parser to the biomedical sublanguage
with a focus on domain terms not found in a general parser lexicon. Using two
biomedical corpora, we implement and evaluate three approaches to addressing
unknown words: automatic lexicon expansion, the use of morphological clues, and
disambiguation using a part-of-speech tagger. We evaluate each approach
separately for its effect on parsing performance and consider combinations of
these approaches. In addition to a 45% increase in parsing efficiency, we find
that the best approach, incorporating information from a domain part-of-speech
tagger, offers a statistically signicant 10% relative decrease in error. The
adapted parser is available under an open-source license at
http://www.it.utu.fi/biolg
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