Please use this identifier to cite or link to this item: https://scholarbank.nus.edu.sg/handle/10635/41953
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dc.titleFeature-rich named entity recognition for bulgarian using conditional random fields
dc.contributor.authorGeorgiev, G.
dc.contributor.authorNakov, P.
dc.contributor.authorGanchev, K.
dc.contributor.authorOsenova, P.
dc.contributor.authorSimov, K.
dc.date.accessioned2013-07-04T08:39:47Z
dc.date.available2013-07-04T08:39:47Z
dc.date.issued2009
dc.identifier.citationGeorgiev, G.,Nakov, P.,Ganchev, K.,Osenova, P.,Simov, K. (2009). Feature-rich named entity recognition for bulgarian using conditional random fields. International Conference Recent Advances in Natural Language Processing, RANLP : 113-117. ScholarBank@NUS Repository.
dc.identifier.issn13138502
dc.identifier.urihttp://scholarbank.nus.edu.sg/handle/10635/41953
dc.description.abstractThe paper presents a feature-rich approach to the automatic recognition and categorization of named entities (persons, organizations, locations, and miscellaneous) in news text for Bulgarian. We combine well-established features used for other languages with language-specific lexical, syntactic and morphological information. In particular, we make use of the rich tagset annotation of the BulTreeBank (680 morpho-syntactic tags), from which we derive suitable task-specific tagsets (local and nonlocal). We further add domain-specific gazetteers and additional unlabeled data, achieving F 1=89.4%, which is comparable to the state-of-the-art results for English.
dc.sourceScopus
dc.subjectConditional random fields
dc.subjectInformation extraction
dc.subjectLinear models
dc.subjectMachine learning
dc.subjectMorphology
dc.subjectNamed entity recognition
dc.typeConference Paper
dc.contributor.departmentCOMPUTER SCIENCE
dc.description.sourcetitleInternational Conference Recent Advances in Natural Language Processing, RANLP
dc.description.page113-117
dc.identifier.isiutNOT_IN_WOS
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