Terminology-based knowledge mining for new knowledge discovery
ACM Transactions on Asian Language Information Processing (TALIP)
Automatic classification of verbs in biomedical texts
ACL-44 Proceedings of the 21st International Conference on Computational Linguistics and the 44th annual meeting of the Association for Computational Linguistics
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Motivation: The sheer volume of textually described biomedical knowledge exerts the need for natural language processing (NLP) applications in order to allow flexible and efficient access to relevant information. Specialized semantic networks (such as biomedical ontologies, terminologies or semantic lexicons) can significantly enhance these applications by supplying the necessary terminological information in a machine-readable form. With the explosive growth of bio-literature, new terms (representing newly identified concepts or variations of the existing terms) may not be explicitly described within the network and hence cannot be fully exploited by NLP applications. Linguistic and statistical clues can be used to extract many new terms from free text. The extracted terms still need to be correctly positioned relative to other terms in the network. Classification as a means of semantic typing represents the first step in updating a semantic network with new terms. Results: The MaSTerClass system implements the case-based reasoning methodology for the classification of biomedical terms. Availability: MaSTerClass is available at http://www.cbr-masterclass.org. It is distributed under an open source licence for educational and research purposes. The software requires Java, JWDSP, Ant, MySQL and X-hive to be installed and licences obtained separately where needed. Contact: i.spasic@manchester.ac.uk Supplementary information: Available at http://www.cbr-masterclass.org