Journals and conference proceedings represent the dominant mechanisms for reporting new biomedical results. The unstructured nature of such publications makes it difficult to utilize data mining or automated knowledge discovery techniques. Annotation (or markup) of these unstructured documents represents the first step in making these documents machine-analyzable. Often, however, the use of similar (or the same) labels for different entities and the use of different labels for the same entity makes entity extraction difficult in biomedical literature. In this paper we present a system called BioAnnotator for identifying and classifying biological terms in documents. BioAnnotator uses domain-based dictionary lookup for recognizing known terms and a rule engine for discovering new terms. We explain how the system uses a biomedical dictionary to learn extraction patterns for the rule engine and how it disambiguates biological terms that belong to multiple semantic classes.
Note: The Institute of Electrical and Electronics Engineers, Incorporated is distributing this Article with permission of the International Business Machines Corporation (IBM) who is the exclusive owner. The recipient of this Article may not assign, sublicense, lease, rent or otherwise transfer, reproduce, prepare derivative works, publicly display or perform, or distribute the Article.