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Development of neural network based rules for confusion set disambiguation in LanguageTool

dc.contributor.authorBrenneis, Markus
dc.contributor.editorBecker, Michael
dc.date.accessioned2019-10-14T11:50:21Z
dc.date.available2019-10-14T11:50:21Z
dc.date.issued2018
dc.description.abstractConfusion set disambiguation is a typical task for grammar checkers like LanguageTool. In this paper we present a neural network based approach which has low memory requirements, high precision with decent recall, and can easily be integrated into LanguageTool. Furthermore, adding support for new confusion pairs does not need any knowledge of the target language. We examine different sampling techniques and neural network architectures and compare our approaches with an existing memory-based algorithm.en
dc.identifier.isbn978-3-88579-448-6
dc.identifier.pissn1614-3213
dc.identifier.urihttps://dl.gi.de/handle/20.500.12116/28978
dc.language.isoen
dc.publisherGesellschaft für Informatik e.V.
dc.relation.ispartofSKILL 2018 - Studierendenkonferenz Informatik
dc.relation.ispartofseriesLecture Notes in Informatics (LNI) - Seminars, Volume S-14
dc.subjectConfusion Set Disambiguation
dc.subjectGrammatical Error Correction
dc.subjectMachine Learning
dc.subjectNeural Networks
dc.titleDevelopment of neural network based rules for confusion set disambiguation in LanguageToolen
dc.typeText/Conference Paper
gi.citation.endPage192
gi.citation.publisherPlaceBonn
gi.citation.startPage181
gi.conference.date26.-27. September 2018
gi.conference.locationBerlin
gi.conference.sessiontitleNeuronale Netze

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