Please use this identifier to cite or link to this item:https://hdl.handle.net/20.500.12259/57508
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dc.contributor.authorBumbulienė, Ieva-
dc.contributor.authorMandravickaitė, Justina-
dc.contributor.authorKrilavičius, Tomas-
dc.coverage.spatialLT-
dc.date.accessioned2018-10-07T01:22:24Z-
dc.date.available2018-10-07T01:22:24Z-
dc.date.issued2017-
dc.identifier.isbn9789986680642-
dc.identifier.otherVDU02-000022155-
dc.identifier.urihttps://hdl.handle.net/20.500.12259/57508-
dc.description.abstractIdentification of Multiword Expressions is an important problem in Natural Language Processing, especially for machine translation and other semantic analysis tasks. Often, lexical association measures (LAM), such as pointwise mutual information (PMI), log likelihood ratio (LLR), Dice are used to identify MWE's. However, just LAMs are insufficient for MWE detection, especially for Lithuanian language, but could be very useful as additional features for Machine Learning (ML) algorithms. Early experiments with Lithuanian and Latvian languages show that using Random Forest with Resample filter, we can achieve almost 99% precision, 58% recall and 73% F-score. We discuss experiments with delfi.lt based corpora, different features, including LAMs, as well as experiments with different ML methods, i.e., Naive Bayes, Random Forests, Support Vector Machines, Artificial Neural Networks and othersen
dc.description.sponsorshipBaltijos pažangių technologijų institutas, Vilnius-
dc.description.sponsorshipBaltijos pažangiųjų technologijų institutas-
dc.description.sponsorshipTaikomosios informatikos katedra-
dc.description.sponsorshipVytauto Didžiojo universitetas-
dc.format.extentp. 10-10-
dc.language.isoen-
dc.relation.ispartofData analysis methods for software systems – DAMSS: 9th International Workshop, Druskininkai, Lithuania, November 30-December 2, 2017 / editor Jolita Bernatavičienė. Vilnius : Vilnius University Institute of Data Science and Digital Technologies, 2017-
dc.subjectMachine learningen
dc.subjectNatural language processingen
dc.subjectMultiword expressionsen
dc.subject.classificationKonferencijų tezės nerecenzuojamuose leidiniuose / Conference theses in non-peer-reviewed publications (T2)-
dc.subject.otherInformatika / Informatics (N009)-
dc.titleApplication of machine learning for MWE identificationen
dc.typeconference paper-
dc.date.updated2018-01-24T11:33Z-
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local.typeT-
item.fulltextNo Fulltext-
item.grantfulltextnone-
crisitem.author.deptInformatikos fakultetas-
crisitem.author.deptTaikomosios informatikos katedra-
crisitem.author.deptTaikomosios informatikos katedra-
Appears in Collections:Universiteto mokslo publikacijos / University Research Publications
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