Identifying terms by their family and friends
COLING '00 Proceedings of the 18th conference on Computational linguistics - Volume 1
Selecting text features for gene name classification: from documents to terms
BioMed '03 Proceedings of the ACL 2003 workshop on Natural language processing in biomedicine - Volume 13
Term identification in the biomedical literature
Journal of Biomedical Informatics - Special issue: Named entity recognition in biomedicine
Mining semantically related terms from biomedical literature
ACM Transactions on Asian Language Information Processing (TALIP)
Biomimetic design through natural language analysis to facilitate cross-domain information retrieval
Artificial Intelligence for Engineering Design, Analysis and Manufacturing
Abordagem não supervisionada para extração de conceitos a partir de textos
Companion Proceedings of the XIV Brazilian Symposium on Multimedia and the Web
Identifying candidates for design-by-analogy
Computers in Industry
Exploring predicate-argument relations for named entity recognition in the molecular biology domain
DS'05 Proceedings of the 8th international conference on Discovery Science
| Hi-index | 0.00 |
In this paper we present an approach to term classification based on verb complementation patterns. The complementation patterns have been automatically learnt by combining information found in a corpus and an ontology, both belonging to the biomedical domain. The learning process is unsupervised and has been implemented as an iterative reasoning procedure based on a partial order relation induced by the domain-specific ontology. First, term recognition was performed by both looking up the dictionary of terms listed in the ontology and applying the C/NC-value method. Subsequently, domain-specific verbs were automatically identified in the corpus. Finally, the classes of terms typically selected as arguments for the considered verbs were induced from the corpus and the ontology. This information was used to classify newly recognised terms. The precision of the classification method reached 64%.