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This article describes our work to identify and classify terms in the domain of molecular biology according to examples that have been marked up by a domain expert in a corpus of abstracts taken from a controlled search of the Medline database. Automatic acquisition of biomedical term lists has so far been slow due to high variability in both the terms and their classification scheme, which we attribute to the diversity of research disciplines involved. Nevertheless, the explosive growth in online molecular biology literature makes a persuasive case for automating many tasks. This includes acquisition of records for gene-product databases such as SwissProt which are currently updated by human experts, a task that is both time consuming and often highly idiosyncratic. In this article we report results from a tool based on a hidden-Markov model for extracting and classifying terms that can be used as a key component in an information extraction system. We discuss the results in light of lexical, syntactic and semantic properties of terms that were revealed by our study.

This article describes our work to identify and classify terms in the domain of molecular biology according to examples that have been marked up by a domain expert in a corpus of abstracts taken from a controlled search of the Medline database. Automatic acquisition of biomedical term lists has so far been slow due to high variability in both the terms and their classification scheme, which we attribute to the diversity of research disciplines involved. Nevertheless, the explosive growth in online molecular biology literature makes a persuasive case for automating many tasks. This includes acquisition of records for gene-product databases such as SwissProt which are currently updated by human experts, a task that is both time consuming and often highly idiosyncratic. In this article we report results from a tool based on a hidden-Markov model for extracting and classifying terms that can be used as a key component in an information extraction system. We discuss the results in light of lexical, syntactic and semantic properties of terms that were revealed by our study.

Motivation: Natural language processing (NLP) methods are regarded as being useful to raise the potential of text mining from biological literature. The lack of an extensively annotated corpus of this literature, however, causes a major bottleneck for applying NLP techniques. GENIA corpus is being developed to provide reference materials to let NLP techniques work for bio-textmining. Results: GENIA corpus version 3.0 consisting of 2000 MEDLINE abstracts has been released with more than 400 000 words and almost 100 000 annotations for biological terms.Availability: GENIA corpus is freely available at http://www-tsujii.is.s.u-tokyo.ac.jp/GENIAKeywords: Text Mining, Information Extraction, Corpus, Natural Language Processing, Computational Molecular Biology

Motivation: Natural language processing (NLP) methods are regarded as being useful to raise the potential of text mining from biological literature. The lack of an extensively annotated corpus of this literature, however, causes a major bottleneck for applying NLP techniques. GENIA corpus is being developed to provide reference materials to let NLP techniques work for bio-textmining. Results: GENIA corpus version 3.0 consisting of 2000 MEDLINE abstracts has been released with more than 400 000 words and almost 100 000 annotations for biological terms. Availability: GENIA corpus is freely available at http://www-tsujii.is.s.u-tokyo.ac.jp/GENIA Keywords: Text Mining, Information Extraction, Corpus, Natural Language Processing, Computational Molecular Biology * To whom correspondence should be addressed.