Word Sense Disambiguation with GermaNet

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dc.contributor.advisor Hinrichs, Erhard (Prof. Dr.)
dc.contributor.author Henrich, Verena
dc.date.accessioned 2015-05-11T07:18:12Z
dc.date.available 2015-05-11T07:18:12Z
dc.date.issued 2015
dc.identifier.other 432622691 de_DE
dc.identifier.uri http://hdl.handle.net/10900/63284
dc.identifier.uri http://nbn-resolving.de/urn:nbn:de:bsz:21-dspace-632846 de_DE
dc.identifier.uri http://dx.doi.org/10.15496/publikation-4706
dc.description.abstract The subject of this dissertation is boosting research on word sense disambiguation (WSD) for German. WSD is a very active area of research in computational linguistics, but most of the work is focused on English. One of the factors that has hampered WSD research for other languages such as German is the lack of appropriate resources, particularly in the form of sense-annotated corpus data. Hence, this work inevitably has to start with the preparation of resources before actual WSD experiments can be performed. The work program is fourfold. Firstly, since sense definitions are necessary to distinguish word senses (both for humans and for automatic WSD algorithms), the German wordnet GermaNet is (semi-)automatically extended with sense descriptions. This is done by automatically mapping GermaNet senses to descriptions in the online dictionary Wiktionary. Secondly, since the availability of sense-annotated corpora is a prerequisite for evaluating and developing word sense disambiguation systems, two GermaNet sense-annotated corpora are constructed. One corpus is automatically constructed and the other corpus is manually sense-annotated. Thirdly, several knowledge-based WSD algorithms are applied and evaluated -- using the newly created sense-annotated corpora. These algorithms are based on a suite of semantic relatedness measures, including path-based, information-content-based, and gloss-based methods. Experiments on gloss-based methods also employ the newly harvested definitions from Wiktionary. Fourthly, several supervised machine learning classifiers are applied to the task of German WSD, including rule-based methods, instance-based methods, probabilistic methods, and support vector machines. The classifiers rely on a wide range of machine learning features and their evaluation focuses on several aspects, including a comparison of several algorithms, a detailed analysis of the implemented features, and an investigation of the influence of syntax and semantics on the disambiguation performance for verbs. en
dc.language.iso en de_DE
dc.publisher Universität Tübingen de_DE
dc.rights ubt-podok de_DE
dc.rights.uri http://tobias-lib.uni-tuebingen.de/doku/lic_mit_pod.php?la=de de_DE
dc.rights.uri http://tobias-lib.uni-tuebingen.de/doku/lic_mit_pod.php?la=en en
dc.subject.classification Disambiguierung , GermaNet , Bedeutung , Computerlinguistik de_DE
dc.subject.ddc 400 de_DE
dc.subject.other Computational Linguistics en
dc.subject.other Word Sense Disambiguation en
dc.subject.other German wordnet en
dc.subject.other sense-annotated corpora en
dc.subject.other GermaNet en
dc.subject.other Wiktionary en
dc.subject.other Computerlinguistik de_DE
dc.subject.other lesartenannotierte Korpora de_DE
dc.subject.other Bedeutungsdisambiguierung de_DE
dc.subject.other deutsches Wortnetz de_DE
dc.title Word Sense Disambiguation with GermaNet en
dc.title Disambiguierung von Wortbedeutungen mit GermaNet de_DE
dc.type PhDThesis de_DE
dcterms.dateAccepted 2015-04-29
utue.publikation.fachbereich Allgemeine u. vergleichende Sprachwissenschaft de_DE
utue.publikation.fakultaet 5 Philosophische Fakultät de_DE

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