Publication | Closed Access
HYENA: Hierarchical Type Classification for Entity Names
103
Citations
25
References
2012
Year
Unknown Venue
Semantic Role LabelingEngineeringPart-of-speech TaggingSemantic WebSemanticsCorpus LinguisticsText MiningNatural Language ProcessingInformation RetrievalData ScienceEntity MentionsComputational LinguisticsHierarchical TaxonomyLanguage StudiesNamed-entity RecognitionMachine TranslationEntity DisambiguationKnowledge DiscoveryTerminology ExtractionHierarchical Type ClassificationDifferent LevelsLinguistics
Inferring lexical type labels for entity mentions in texts is an important asset for NLP tasks like semantic role labeling and named entity disambiguation (NED). Prior work has focused on flat and relatively small type systems where most entities belong to exactly one type. This paper addresses very fine-grained types organized in a hierarchical taxonomy, with several hundreds of types at different levels. We present HYENA for multi-label hierarchical classification. HYENA exploits gazetteer features and accounts for the joint evidence for types at different levels. Experiments and an extrinsic study on NED demonstrate the practical viability of HYENA.
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