Extraction and use of linguistic patterns for modelling medical guidelines
Artificial Intelligence in Medicine
The double role of ontologies in information science research: Research Articles
Journal of the American Society for Information Science and Technology
International Journal of Bioinformatics Research and Applications
Learning context-free grammars to extract relations from text
Proceedings of the 2008 conference on ECAI 2008: 18th European Conference on Artificial Intelligence
BioNLP '06 Proceedings of the Workshop on Linking Natural Language Processing and Biology: Towards Deeper Biological Literature Analysis
BioNoculars: extracting protein-protein interactions from biomedical text
BioNLP '07 Proceedings of the Workshop on BioNLP 2007: Biological, Translational, and Clinical Language Processing
LNLBioNLP '06 Proceedings of the HLT-NAACL BioNLP Workshop on Linking Natural Language and Biology
Learning relations from biomedical corpora using dependency trees
KDECB'06 Proceedings of the 1st international conference on Knowledge discovery and emergent complexity in bioinformatics
Arguments of nominals in semantic interpretation of biomedical text
BioNLP '10 Proceedings of the 2010 Workshop on Biomedical Natural Language Processing
Development and evaluation of a biomedical search engine using a predicate-based vector space model
Journal of Biomedical Informatics
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The increasing amount of publicly available literature and experimental data in biomedicine makes it hard for biomedical researchers to stay up-to-date. Genescene is a toolkit that will help alleviate this problem by providing an overview of published literature content. We combined a linguistic parser with Concept Space, a co-occurrence based semantic net. Both techniques extract complementary biomedical relations between noun phrases from MEDLINE abstracts. The parser extracts precise and semantically rich relations from individual abstracts. Concept Space extracts relations that hold true for the collection of abstracts. The Gene Ontology, the Human Genome Nomenclature, and the Unified Medical Language System, are also integrated in Genescene. Currently, they are used to facilitate the integration of the two relation types, and to select the more interesting and high-quality relations for presentation. A user study focusing on p53 literature is discussed. All MEDLINE abstracts discussing p53 were processed in Genescene. Two researchers evaluated the terms and relations from several abstracts of interest to them. The results show that the terms were precise (precision 93%) and relevant, as were the parser relations (precision 95%). The Concept Space relations were more precise when selected with ontological knowledge (precision 78%) than without (60%). © 2005 Wiley Periodicals, Inc.