Lexical classes, when tailored to the application and domain in question, can provide an effective means to deal with a number of natural language processing (NLP) tasks. While manual construction of such classes is difficult, recent research shows that it is possible to automatically induce verb classes from cross-domain corpora with promising accuracy. We report a novel experiment where similar technology is applied to the important, challenging domain of biomedicine. We show that the resulting classification, acquired from a corpus of biomedical journal articles, is highly accurate and strongly domain-specific. It can be used to aid BIO-NLP directly or as useful material for investigating the syntax and semantics of verbs in biomedical texts. © 2006 Association for Computational Linguistics.
CITATION STYLE
Korhonen, A., Krymolowski, Y., & Collier, N. (2006). Automatic classification of verbs in biomedical texts. In COLING/ACL 2006 - 21st International Conference on Computational Linguistics and 44th Annual Meeting of the Association for Computational Linguistics, Proceedings of the Conference (Vol. 1, pp. 345–352). Association for Computational Linguistics (ACL). https://doi.org/10.3115/1220175.1220219
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