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Volume 5

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In Silico Biology 5, 0008 (2004); ©2005, Bioinformation Systems e.V.  



Utilizing weakly controlled vocabulary for sentence segmentation in biomedical literature

Kenji Satou1, 2,* and Kaoru Yamamoto3

1 School of Knowledge Science, Japan Advanced Institute of Science and Technology
2 BIRD, Japan Science and Technology Agency (JST)
3 CREST, Japan Science and Technology Agency (JST)

* Corresponding author; Email: ken@jaist.ac.jp


Edited by E. Wingender; received January 14, 2005; revised and accepted March 03, 2005; published March 18, 2005


Abstract

Since biomedical texts contain a wide variety of domain specific terms, building a large dictionary to perform term matching is of great relevance. However, due to the existence of null boundary between adjacent terms, this matching is not a trivial problem. Moreover, it is known that generative words cannot be comprehensively included in a dictionary because their possible variations are infinite.

In this study, we report our approach to dictionary building and term matching in biomedical texts. Large amount of terms with/without part-of-speech (POS) and/or category information were gathered, and a completion program generated ~1.36 million term variants to avoid stemming problems when matching terms. The dictionary was stored in a relational database management system (RDBMS) for quick lookup, and used by a matching program. Since the matching operation is not restricted to a substring surrounded by space characters, we can avoid the problem of null boundaries. This feature is also useful for generative words. Experimental results on GENIA corpus are promising: nearly half of the possible terms were correctly recognized as a meaningful segment, and most of the remaining half could be correctly recognized by some post-processing process, like chunking and further decomposition. It should be remarked that although we have not used term cost, connectivity cost, or syntactic information, reasonable segmentation and dictionary lookup were performed in most cases.

Keywords: text processing, dictionary building, dictionary lookup and matching, sentence segmentation, term boundary