Please use this identifier to cite or link to this item: https://hdl.handle.net/10356/155193
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dc.contributor.authorZhong, Xiaoshien_US
dc.contributor.authorCambria, Eriken_US
dc.contributor.authorHussain Amiren_US
dc.date.accessioned2022-02-16T07:43:34Z-
dc.date.available2022-02-16T07:43:34Z-
dc.date.issued2020-
dc.identifier.citationZhong, X., Cambria, E. & Hussain Amir (2020). Extracting time expressions and named entities with constituent-based tagging schemes. Cognitive Computation, 12(4), 844-862. https://dx.doi.org/10.1007/s12559-020-09714-8en_US
dc.identifier.issn1866-9956en_US
dc.identifier.urihttps://hdl.handle.net/10356/155193-
dc.description.abstractTime expressions and named entities play important roles in data mining, information retrieval, and natural language processing. However, the conventional position-based tagging schemes (e.g., the BIO and BILOU schemes) that previous research used to model time expressions and named entities suffer from the problem of inconsistent tag assignment. To overcome the problem of inconsistent tag assignment, we designed a new type of tagging schemes to model time expressions and named entities based on their constituents. Specifically, to model time expressions, we defined a constituent-based tagging scheme termed TOMN scheme with four tags, namely T, O, M, and N, indicating the defined constituents of time expressions, namely time token, modifier, numeral, and the words outside time expressions. To model named entities, we defined a constituent-based tagging scheme termed UGTO scheme with four tags, namely U, G, T, and O, indicating the defined constituents of named entities, namely uncommon word, general modifier, trigger word, and the words outside named entities. In modeling, our TOMN and UGTO schemes model time expressions and named entities under conditional random fields with minimal features according to an in-depth analysis for the characteristics of time expressions and named entities. Experiments on diverse datasets demonstrate that our proposed methods perform equally with or more effectively than representative state-of-the-art methods on both time expression extraction and named entity extraction.en_US
dc.description.sponsorshipAgency for Science, Technology and Research (A*STAR)en_US
dc.language.isoenen_US
dc.relationA18A2b0046en_US
dc.relation.ispartofCognitive Computationen_US
dc.rights© 2020 Springer Science+Business Media, LLC, part of Springer Nature. All rights reserved.en_US
dc.subjectEngineering::Computer science and engineeringen_US
dc.titleExtracting time expressions and named entities with constituent-based tagging schemesen_US
dc.typeJournal Articleen
dc.contributor.schoolSchool of Computer Science and Engineeringen_US
dc.identifier.doi10.1007/s12559-020-09714-8-
dc.identifier.scopus2-s2.0-85084445501-
dc.identifier.issue4en_US
dc.identifier.volume12en_US
dc.identifier.spage844en_US
dc.identifier.epage862en_US
dc.subject.keywordsInconsistent Tag Assignmenten_US
dc.subject.keywordsPosition-Based Tagging Schemeen_US
dc.description.acknowledgementThis research was funded by the AME Programmatic Funding (Project No. A18A2b0046) from the Agency for Science, Technology and Research (A*STAR), Singapore.en_US
item.fulltextNo Fulltext-
item.grantfulltextnone-
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