Learning meronyms from biomedical text

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Abstract

The part-whole relation is of special importance in biomedicine: structure and process are organised along partitive axes. Anatomy, for example, is rich in partwhole relations. This paper reports preliminary experiments on part-whole extraction from a corpus of anatomy definitions, using a fully automatic iterative algorithm to learn simple lexico-syntactic patterns from multiword terms. The experiments show that meronyms can be extracted using these patterns. A failure analysis points out factors that could contribute to improvements in both precision and recall, including pattern generalisation, pattern pruning, and term matching. The analysis gives insights into the relationship between domain terminology and lexical relations, and into evaluation strategies for relation learning. © 2005 Association for Computational Linguistics.

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APA

Roberts, A. (2005). Learning meronyms from biomedical text. In ACL-05 - 43rd Annual Meeting of the Association for Computational Linguistics, Proceedings of the Conference (pp. 49–54). Association for Computational Linguistics (ACL). https://doi.org/10.3115/1628960.1628971

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