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dc.contributor.authorNguyen, Hung D.-
dc.contributor.authorCao, Tru H.-
dc.date.accessioned2019-06-27T09:13:06Z-
dc.date.available2019-06-27T09:13:06Z-
dc.date.issued2018-
dc.identifier.citationNguyen, H. D.; Cao, T. H. (2018). Coreference Resolution in Vietnamese Electronic Medical Records. Journal of Science: Comp. Science & Com. Eng., Vol. 34, No. 2 (2018) 33–43.vi
dc.identifier.issn2588-1086-
dc.identifier.urihttp://repository.vnu.edu.vn/handle/VNU_123/64762-
dc.description.abstractElectronic medical records (EMR) have emerged as an important source of data for research in medicine and information technology, as they contain much of valuable human medical knowledge in healthcare and patient treatment. This paper tackles the problem of coreference resolution in Vietnamese EMRs. Unlike in English ones, in Vietnamese clinical texts, verbs are often used to describe disease symptoms. So we first define rules to annotate verbs as mentions and consider coreference between verbs and other noun or adjective mentions possible. Then we propose a support vector machine classifier on bag-of-words vector representation of mentions that takes into account the special characteristics of Vietnamese language to resolve their coreference. The achieved F1 score on our dataset of real Vietnamese EMRs provided by a hospital in Ho Chi Minh city is 91.4%. To the best of our knowledge, this is the first research work in coreference resolution on Vietnamese clinical textsvi
dc.language.isoenvi
dc.publisherH. : ĐHQGHNvi
dc.relation.ispartofseriesJournal of Science: Comp. Science & Com. Eng.;-
dc.subjectClinical textvi
dc.subjectsupport vector machinevi
dc.subjectbag-of-words vectorvi
dc.subjectlexical similarityvi
dc.subjectunrestricted coreferencevi
dc.titleCoreference Resolution in Vietnamese Electronic Medical Recordsvi
dc.typeArticlevi
dc.identifier.doihttps://doi.org/10.25073/2588-1086/vnucsce.210-
Appears in Collections:Computer Science and Communication Engineering


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  • Full metadata record
    DC FieldValueLanguage
    dc.contributor.authorNguyen, Hung D.-
    dc.contributor.authorCao, Tru H.-
    dc.date.accessioned2019-06-27T09:13:06Z-
    dc.date.available2019-06-27T09:13:06Z-
    dc.date.issued2018-
    dc.identifier.citationNguyen, H. D.; Cao, T. H. (2018). Coreference Resolution in Vietnamese Electronic Medical Records. Journal of Science: Comp. Science & Com. Eng., Vol. 34, No. 2 (2018) 33–43.vi
    dc.identifier.issn2588-1086-
    dc.identifier.urihttp://repository.vnu.edu.vn/handle/VNU_123/64762-
    dc.description.abstractElectronic medical records (EMR) have emerged as an important source of data for research in medicine and information technology, as they contain much of valuable human medical knowledge in healthcare and patient treatment. This paper tackles the problem of coreference resolution in Vietnamese EMRs. Unlike in English ones, in Vietnamese clinical texts, verbs are often used to describe disease symptoms. So we first define rules to annotate verbs as mentions and consider coreference between verbs and other noun or adjective mentions possible. Then we propose a support vector machine classifier on bag-of-words vector representation of mentions that takes into account the special characteristics of Vietnamese language to resolve their coreference. The achieved F1 score on our dataset of real Vietnamese EMRs provided by a hospital in Ho Chi Minh city is 91.4%. To the best of our knowledge, this is the first research work in coreference resolution on Vietnamese clinical textsvi
    dc.language.isoenvi
    dc.publisherH. : ĐHQGHNvi
    dc.relation.ispartofseriesJournal of Science: Comp. Science & Com. Eng.;-
    dc.subjectClinical textvi
    dc.subjectsupport vector machinevi
    dc.subjectbag-of-words vectorvi
    dc.subjectlexical similarityvi
    dc.subjectunrestricted coreferencevi
    dc.titleCoreference Resolution in Vietnamese Electronic Medical Recordsvi
    dc.typeArticlevi
    dc.identifier.doihttps://doi.org/10.25073/2588-1086/vnucsce.210-
    Appears in Collections:Computer Science and Communication Engineering


  • 210-1-891-3-10-20190109.pdf
    • Size : 1,92 MB

    • Format : Adobe PDF

    • View : 
    • Download :