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- Alternative URL to paper: http://www.gabormelli.com/Publications/2010/2010_SupervisedIdentCMentionsAndLinkingToOntology/2010_SupervisedIdentCMentionsAndLinkingToOntology.pdf
- Google Scholar: ~2 Citations
- ACM DL: No Citations Available.
- Semantic Scholar: ~ 4 Citations (Retrieved:2019-09-29).
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We propose a purely supervised learning approach to the task of identifying concept mentions within a document and of linking these mentions to their corresponding concept in a given ontology. Concept mention identification is performed with a trained CRF sequential model. Each mention is associated with a set of candidate ontology concepts, and binary training feature vectors are generated for these pairings. We formalize the feature space to expand on those those proposed in the literature, and also propose the inclusion of features derived from the training corpus. Iterative classification is proposed as a method of handling collective decisions in a supervised manner. The approach, named SCMILO, is validated against the ability to identify the concept mentions within the 139 KDD-2009 conference paper abstracts, and to link these mentions to a domain-specific ontology for the field of data mining.
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|2010 SupervisedIdentCMentionsAndLinkingToOntology||Gabor Melli|
|Supervised Identification and Linking of Concept Mentions to a Domain-Specific Ontology||Proceedings of the 19th ACM International Conference on Information and Knowledge Management||http://dl.acm.org/authorize?399852||10.1145/1871437.1871712||2010|
|Author||Gabor Melli + and Martin Ester +|
|journal||Proceedings of the 19th ACM International Conference on Information and Knowledge Management +|
|title||Supervised Identification and Linking of Concept Mentions to a Domain-Specific Ontology +|