Extraction of semantic biomedical relations from text using conditional random fields |
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Authors: | Markus Bundschus Mathaeus Dejori Martin Stetter Volker Tresp Hans-Peter Kriegel |
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Affiliation: | (1) Institute for Computer Science, Ludwig-Maximilians-University Munich, Oettingenstr. 67, 80538 Munich, Germany;(2) Siemens AG, Corporate Technology, Information and Communications, Otto-Hahn-Ring 6, 81739 Munich, Germany;(3) Integrated Data Systems Department, Siemens Corporate Research, 755 College Road East, Princeton New Jersey, 08540, USA |
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Abstract: | Background The increasing amount of published literature in biomedicine represents an immense source of knowledge, which can only efficiently be accessed by a new generation of automated information extraction tools. Named entity recognition of well-defined objects, such as genes or proteins, has achieved a sufficient level of maturity such that it can form the basis for the next step: the extraction of relations that exist between the recognized entities. Whereas most early work focused on the mere detection of relations, the classification of the type of relation is also of great importance and this is the focus of this work. In this paper we describe an approach that extracts both the existence of a relation and its type. Our work is based on Conditional Random Fields, which have been applied with much success to the task of named entity recognition. |
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