Semantic classification of bio-entities incorporating predicate argument features
DC Field | Value | Language |
---|---|---|
dc.contributor.author | Park, Kyung-Mi | - |
dc.contributor.author | Rim, Hae-Chang | - |
dc.date.accessioned | 2021-09-09T09:39:40Z | - |
dc.date.available | 2021-09-09T09:39:40Z | - |
dc.date.created | 2021-06-10 | - |
dc.date.issued | 2008-04 | - |
dc.identifier.issn | 0916-8532 | - |
dc.identifier.uri | https://scholar.korea.ac.kr/handle/2021.sw.korea/123793 | - |
dc.description.abstract | In this paper, we propose new external context features for the semantic classification of bio-entities. In the previous approaches, the words located on the left or the right context of bio-entities are frequently used as the external context features. However, in our prior experiments, the external contexts in a flat representation did not improve the performance. In this study, we incorporate predicate-argument features into training the ME-based classifier. Through parsing and argument identification, we recognize biomedical verbs that have argument relations with the constituents including a bio-entity, and then use the predicate-argument structures as the external context features. The extraction of predicate-argument features can be done by performing two identification tasks: the biomedically salient word identification which determines whether a word is a biomedically salient word or not, and the target verb identification which identifies biomedical verbs that have argument relations with the constituents including a bio-entity. Experiments show that the performance of semantic classification in the bio domain can be improved by utilizing such predicate-argument features. | - |
dc.language | English | - |
dc.language.iso | en | - |
dc.publisher | IEICE-INST ELECTRONICS INFORMATION COMMUNICATIONS ENG | - |
dc.title | Semantic classification of bio-entities incorporating predicate argument features | - |
dc.type | Article | - |
dc.contributor.affiliatedAuthor | Rim, Hae-Chang | - |
dc.identifier.doi | 10.1093/ietisy/e91-d.4.1211 | - |
dc.identifier.scopusid | 2-s2.0-68149169755 | - |
dc.identifier.wosid | 000255648700040 | - |
dc.identifier.bibliographicCitation | IEICE TRANSACTIONS ON INFORMATION AND SYSTEMS, v.E91D, no.4, pp.1211 - 1214 | - |
dc.relation.isPartOf | IEICE TRANSACTIONS ON INFORMATION AND SYSTEMS | - |
dc.citation.title | IEICE TRANSACTIONS ON INFORMATION AND SYSTEMS | - |
dc.citation.volume | E91D | - |
dc.citation.number | 4 | - |
dc.citation.startPage | 1211 | - |
dc.citation.endPage | 1214 | - |
dc.type.rims | ART | - |
dc.type.docType | Article | - |
dc.description.journalClass | 1 | - |
dc.description.journalRegisteredClass | scie | - |
dc.description.journalRegisteredClass | scopus | - |
dc.relation.journalResearchArea | Computer Science | - |
dc.relation.journalWebOfScienceCategory | Computer Science, Information Systems | - |
dc.relation.journalWebOfScienceCategory | Computer Science, Software Engineering | - |
dc.subject.keywordAuthor | semantic classification | - |
dc.subject.keywordAuthor | predicate-argument feature | - |
dc.subject.keywordAuthor | biomedical verb | - |
dc.subject.keywordAuthor | maximum entropy model | - |
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