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dc.contributor.authorHettinger, Lena
dc.contributor.authorZehe, Albin
dc.contributor.authorDallmann, Alexander
dc.contributor.authorHotho, Andreas
dc.contributor.editorDavid, Klaus
dc.contributor.editorGeihs, Kurt
dc.contributor.editorLange, Martin
dc.contributor.editorStumme, Gerd
dc.date.accessioned2019-08-27T12:55:21Z
dc.date.available2019-08-27T12:55:21Z
dc.date.issued2019
dc.identifier.isbn978-3-88579-688-6
dc.identifier.issn1617-5468
dc.identifier.urihttp://dl.gi.de/handle/20.500.12116/24971
dc.description.abstractIn recent years, there has been an increasing interest in the task of relation classification, which aims to label a relation between two semantic entities. In this work, we investigate how domain-specific information influences the performance of ClaiRE, an SVM-based system combining manually crafted features with word embeddings. To this end, we experiment with a wide range of word embeddings and evaluate on one general and two scientific relation classification datasets. We release all of our code for relation classification and data for scientific word embeddings to enable the reproduction of our experiments.en
dc.language.isoen
dc.publisherGesellschaft für Informatik e.V.
dc.relation.ispartofINFORMATIK 2019: 50 Jahre Gesellschaft für Informatik – Informatik für Gesellschaft
dc.relation.ispartofseriesLecture Notes in Informatics (LNI) - Proceedings, Volume P-294
dc.subjectword embedding
dc.subjectrelation classification
dc.subjectcontext sensitive
dc.subjectdomain specific
dc.titleEClaiRE: Context Matters! – Comparing Word Embeddings for Relation Classificationen
dc.typeText/Conference Paper
dc.pubPlaceBonn
mci.reference.pages191-204
mci.conference.sessiontitleData Science
mci.conference.locationKassel
mci.conference.date23.-26. September 2019
dc.identifier.doi10.18420/inf2019_24


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