Automatic Classification of Verbs in Biomedical Texts

Proceedings of the 21st International Conference on Computational Linguistics and 44th Annual Meeting of the ACL, pages 345–352, Sydney, July 2006. c⃝2006 Association for Computational Linguistics Automatic Classification of Verbs in Biomedical Texts Anna Korhonen University of Cambridge Computer Laboratory 15 JJ Thomson Avenue Cambridge CB3 0GD, UK alk23@cl.cam.ac.uk Yuval Krymolowski Dept. of Computer Science Technion Haifa 32000 Israel yuvalkr@cs.technion.ac.il Nigel Collier National Institute of Informatics Hitotsubashi 2-1-2 Chiyoda-ku, Tokyo 101-8430 Japan collier@nii.ac.jp Abstract Lexical classes, when tailored to the appli- cation and domain in question, can provide an effective means to deal with a num- ber 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 technol- ogy is applied to the important, challeng- ing 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 investi- gating the syntax and semantics of verbs in biomedical texts. 1 Introduction Lexical classes which capture the close relation between the syntax and semantics of verbs have attracted considerable interest in NLP (Jackendoff, 1990; Levin, 1993; Dorr, 1997; Prescher et al., 2000). Such classes are useful for their ability to capture generalizations about a range of linguis- tic properties. For example, verbs which share the meaning of ‘manner of motion’ (such as travel, run, walk), behave similarly also in terms of subcategorization (I traveled/ran/walked, I trav- eled/ran/walked to London, I traveled/ran/walked five miles). Although the correspondence between the syntax and semantics of words is not perfect and the classes do not provide means for full se- mantic inferencing, their predictive power is nev- ertheless considerable. NLP systems can benefit from lexical classes in many ways. Such classes define the mapping from surface realization of arguments to predicate- argument structure, and are therefore an impor- tant component of any system which needs the latter. As the classes can capture higher level abstractions they can be used as a means to ab- stract away from individual words when required. They are also helpful in many operational contexts where lexical information must be acquired from small application-specific corpora. Their predic- tive power can help compensate for lack of data fully exemplifying the behavior of relevant words. Lexical verb classes have been used to sup- port various (multilingual) tasks, such as compu- tational lexicography, language generation, ma- chine translation, word sense disambiguation, se- mantic role labeling, and subcategorization acqui- sition (Dorr, 1997; Prescher et al., 2000; Korho- nen, 2002). However, large-scale exploitation of the classes in real-world or domain-sensitive tasks has not been possible because the existing classi- fications, e.g. (Levin, 1993), are incomprehensive and unsuitable for specific domains. While manual classification of large numbers of words has proved difficult and time-consuming, recent research shows that it is possible to auto- …

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