Integration of linguistic and web information to improve biomedical terminology extraction
Comprehensive terminology is essential for a community to describe, exchange, and retrieve data. In multiple domain, the explosion of text data produced has reached a level for which automatic terminology extraction and enrichment is mandatory. Automatic Term Extraction (or Recognition) methods use natural language processing to do so. Methods featuring linguistic and statistical aspects as often proposed in the literature, solve some problems related to term extraction as low frequency, complexity of the multi-word term extraction, human effort to validate candidate terms. In contrast, we present two new measures for extracting and ranking muli-word terms from domain-specific corpora, covering the all mentioned problems. In addition we demonstrate how the use of the Web to evaluate the significance of a multi-word term candidate, helps us to outperform precision results obtain on the biomedical GENIA corpus with previous reported measures such as C-value.
| Main Authors: | , , , |
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| Format: | conference_item biblioteca |
| Language: | eng |
| Published: |
ACM
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| Subjects: | C30 - Documentation et information, 000 - Autres thèmes, |
| Online Access: | http://agritrop.cirad.fr/574172/ http://agritrop.cirad.fr/574172/1/document_574172.pdf |
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