Lexical adaptation of link grammar to the biomedical sublanguage: a comparative evaluation of three approaches.
BACKGROUND
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.


RESULTS
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 significant 10% relative decrease in error.


CONCLUSIONS
When available, a high-quality domain part-of-speech tagger is the best solution to unknown word issues in the domain adaptation of a general parser. In the absence of such a resource, surface clues can provide remarkably good coverage and performance when tuned to the domain. The adapted parser is available under an open-source license.
Pyysalo
Sampo
S
Turku Centre for Computer Science (TUCS) and University of Turku, Lemminkäisenkatu 14 A, 20520 Turku, Finland. sampo.pyysalo@it.utu.fi
Salakoski
Tapio
T
Aubin
Sophie
S
Nazarenko
Adeline
A
D000043:Abstracting and Indexing; D000465:Algorithms; D001185:Artificial Intelligence; D016208:Databases, Factual; D016247:Information Storage and Retrieval / Q000379:methods*; D009323:Natural Language Processing; D012660:Semantics; D012984:Software; D009626:Terminology as Topic; D018875:Vocabulary, Controlled