What makes a gene name? Named entity recognition in the biomedical literature
A Professor for Knowledge Management in Bioinformatics at the Humboldt-Universität in Berlin. His main topics of research are semantic data integration, text mining and biomedical data management.
A Researcher in the group of Ulf Leser. His work in text mining focuses on information extraction for life science applications, especially for systems biology.
Ulf Leser, Department for Computer Science, Humboldt-Universität zu Berlin, Rudower Chaussee 25, 12489 Berlin, Germany Tel: +49 30 2093 3902 Fax: +49 30 2093 5484 E-mail: leser{at}informatik.huberlin.de
The recognition of biomedical concepts in natural text (named entity recognition, NER) is a key technology for automatic or semi-automatic analysis of textual resources. Precise NER tools are a prerequisite for many applications working on text, such as information retrieval, information extraction or document classification. Over the past years, the problem has achieved considerable attention in the bioinformatics community and experience has shown that NER in the life sciences is a rather difficult problem. Several systems and algorithms have been devised and implemented. In this paper, the problems and resources in NER research are described, the principal algorithms underlying most systems sketched, and the current state-of-the-art in the field surveyed.
Keywords: text mining, knowledge management, information extraction, machine learning, named entity recognition
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