Please use this identifier to cite or link to this item:https://hdl.handle.net/20.500.12259/59817
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dc.contributor.authorBumbulienė, Ieva-
dc.contributor.authorMandravickaitė, Justina-
dc.contributor.authorBielinskienė, Agnė-
dc.contributor.authorBoizou, Loic-
dc.contributor.authorKovalevskaitė, Jolanta-
dc.contributor.authorRimkutė, Erika-
dc.contributor.authorVilkaitė-Lozdienė, Laura-
dc.contributor.authorMan, Ka Lok-
dc.contributor.authorKrilavičius, Tomas-
dc.coverage.spatialTW-
dc.date.accessioned2018-11-22T17:46:43Z-
dc.date.available2018-11-22T17:46:43Z-
dc.date.issued2018-
dc.identifier.issn2223523X-
dc.identifier.otherVDU02-000022987-
dc.identifier.urihttp://ijdatics.datics.net/current_issues.html-
dc.descriptioneISSN 2071-2987. This research was funded by the Research Council of Lithuania (No. LIP-027/2016)). This volume is comprised of research papers from the International Conference on Recent Advancements in Computing, Internet of Things (IoT) and Computer Engineering Technology (CICET), October 29-31, 2018, Taipei, Taiwan. CICET 2018 is hosted and organized by The Tamkang University-
dc.description.abstractWe discuss an experiment on automatic identification of multiword expressions (MWEs) in Lithuanian corpus. Our training dataset was annotated morphologically (POS tagger). It was manually annotated with MWEs by 4 linguists as well. We also used word embeddings in our feature set. Deep learning methods are widely used in many NLP tasks and applications including MWEs identification. Thus, our experimental setup included deep learning methods (Recurrent Neural Networks; RNNs) and was used for automatic identification of contiguous and non-contiguous MWEs of different length. Best results (44.9% F1-Score) were achieved with RNNs and Stochastic Gradient Descent as optimizer together with Categorical Cross Entropy as loss functionen
dc.description.sponsorshipBaltijos pažangių technologijų institutas, Vilnius-
dc.description.sponsorshipBaltijos pažangiųjų technologijų institutas-
dc.description.sponsorshipLituanistikos katedra-
dc.description.sponsorshipTaikomosios informatikos katedra-
dc.description.sponsorshipUžsienio kalbų, lit. ir vert. s. katedra-
dc.description.sponsorshipVytauto Didžiojo universitetas-
dc.format.extentp. 44-47-
dc.language.isoen-
dc.relation.ispartofInternational journal of design, analysis and tools for integrated circuits and systems (IJDATICS). Hong Kong : Solari Co, 2018, vol. 7, no. 1-
dc.relation.isreferencedbyApplied Science & Technology Source (EBSCO)-
dc.relation.isreferencedbyAcademic Search R&D (EBSCO)-
dc.subjectContiguous multiword expressionsen
dc.subjectNon-contiguous multiword expressionsen
dc.subjectRecurrent neural networksen
dc.subjectLithuanian corpusen
dc.subject.classificationStraipsnis kitose duomenų bazėse / Article in other databases (S4)-
dc.subject.otherInformatika / Informatics (N009)-
dc.titleRNNs for Lithuanian multiword expressions identificationen
dc.typeresearch article-
dcterms.bibliographicCitation22-
dc.date.updated2019-07-09T11:10Z-
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item.grantfulltextopen-
item.fulltextWith Fulltext-
crisitem.author.deptInformatikos fakultetas-
crisitem.author.deptTaikomosios informatikos katedra-
crisitem.author.deptKompiuterinės lingvistikos centras-
crisitem.author.deptUžsienio kalbų, literatūros ir vertimo studijų katedra-
crisitem.author.deptUžsienio kalbų, literatūros ir vertimo studijų katedra-
crisitem.author.deptLituanistikos katedra-
crisitem.author.deptTaikomosios informatikos katedra-
Appears in Collections:Universiteto mokslo publikacijos / University Research Publications
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