Topic Modeling for Word Sense Induction

Knopp, Johannes ; Völker, Johanna ; Ponzetto, Simone Paolo

Document Type: Conference or workshop publication
Year of publication: 2013
Book title: Language Processing and Knowledge in the Web : 25th International Conference, GSCL 2013, Darmstadt, Germany, September 25-27, 2013, Proceedings
The title of a journal, publication series: Lecture Notes in Computer Science
Volume: 8105
Page range: 97-103
Date of the conference: Sept. 25-27, 2013
Publisher: Gurevych, Iryna
Place of publication: Berlin [u.a.]
Publishing house: Springer
ISBN: 978-3-642-40721-5 , 978-3-642-40722-2
ISSN: 0302-9743 , 1611-3349
Publication language: English
Institution: School of Business Informatics and Mathematics > Practical Computer Science II: Artificial Intelligence (Stuckenschmidt 2009-)
Subject: 004 Computer science, internet
Keywords (English): word sense induction , topic models , lexical semantics
Abstract: In this paper, we present a novel approach to Word Sense Induction which is based on topic modeling. Key to our methodology is the use of word-topic distributions as a means to estimate sense distribu- tions. We provide these distributions as input to a clustering algorithm in order to automatically distinguish between the senses of semantically ambiguous words. The results of our evaluation experiments indicate that the performance of our approach is comparable to state-of-the-art methods whose sense distinctions are not as easily interpretable.

Dieser Eintrag ist Teil der Universitätsbibliographie.

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