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dc.contributor.authorPlum, Alistair
dc.contributor.authorRanasinghe, Tharindu
dc.contributor.authorOrăsan, Constantin
dc.date.accessioned2019-09-06T13:36:09Z
dc.date.available2019-09-06T13:36:09Z
dc.date.issued2019-09-02
dc.identifier.citationPlum, A., Ranasinghe, T. and Orasan, C. (2019) Toponym detection in the bio-medical domain: A hybrid approach with deep learning. RANLP 2017, 2nd-4th September, Varna, Bulgaria.en
dc.identifier.isbn9789544520557
dc.identifier.issn1313-8502
dc.identifier.urihttp://hdl.handle.net/2436/622706
dc.description.abstractThis paper compares how different machine learning classifiers can be used together with simple string matching and named entity recognition to detect locations in texts. We compare five different state-of-the-art machine learning classifiers in order to predict whether a sentence contains a location or not. Following this classification task, we use a string matching algorithm with a gazetteer to identify the exact index of a toponym within the sentence. We evaluate different approaches in terms of machine learning classifiers, text pre-processing and location extraction on the SemEval-2019 Task 12 dataset, compiled for toponym resolution in the bio-medical domain. Finally, we compare the results with our system that was previously submitted to the SemEval-2019 task evaluation.en
dc.formatapplication/PDFen
dc.language.isoenen
dc.publisherRANLPen
dc.relation.urlhttp://lml.bas.bg/ranlp2019/proceedings-ranlp-2019.pdfen
dc.subjecttoponymy detectionen
dc.subjectdeep learningen
dc.titleToponym detection in the bio-medical domain: A hybrid approach with deep learningen
dc.typeConference contributionen
dc.date.updated2019-09-02T22:47:40Z
dc.conference.nameRANLP 2019
dc.conference.locationVarna, Bulgaria
pubs.finish-date2019-09-04
pubs.finish-date2019-09-04
pubs.start-date2019-09-02
pubs.start-date2019-09-02
dc.date.accepted2019-07-06
rioxxterms.funderUniversity of Wolverhamptonen
rioxxterms.identifier.projectUOW060919COen
rioxxterms.versionAMen
rioxxterms.licenseref.urihttps://creativecommons.org/licenses/by/4.0/en
rioxxterms.licenseref.startdate2019-09-06en
dc.description.versionPublished version
refterms.dateFCD2019-09-06T11:25:51Z
refterms.versionFCDAM
refterms.dateFOA2019-09-06T13:36:10Z


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