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A Language-independent Sense Clustering Approach for Enhanced WSD

Matuschek, Michael ; Miller, Tristan ; Gurevych, Iryna
Hrsg.: Ruppenhofer, Josef ; Faaß, Gertrud (2014)
A Language-independent Sense Clustering Approach for Enhanced WSD.
Hildesheim, Germany
Konferenzveröffentlichung, Bibliographie

Kurzbeschreibung (Abstract)

We present a method for clustering word senses of a lexical-semantic resource by mapping them to those of another sense inventory. This is a promising way of reducing polysemy in sense inventories and consequently improving word sense disambiguation performance. In contrast to previous approaches, we use Dijkstra-WSA, a parameterizable alignment algorithm which is largely resource- and language-agnostic. To demonstrate this, we apply our technique to GermaNet, the German equivalent to WordNet. The GermaNet sense clusterings we induce through alignments to various collaboratively constructed resources achieve a significant boost in accuracy, even though our method is far less complex and less dependent on language-specific knowledge than past approaches.

Typ des Eintrags: Konferenzveröffentlichung
Erschienen: 2014
Herausgeber: Ruppenhofer, Josef ; Faaß, Gertrud
Autor(en): Matuschek, Michael ; Miller, Tristan ; Gurevych, Iryna
Art des Eintrags: Bibliographie
Titel: A Language-independent Sense Clustering Approach for Enhanced WSD
Sprache: Englisch
Publikationsjahr: Oktober 2014
Verlag: Universitätsverlag Hildesheim
Buchtitel: Proceedings of the 12th Konferenz zur Verarbeitung natürlicher Sprache (KONVENS 2014)
Veranstaltungsort: Hildesheim, Germany
URL / URN: https://fileserver.ukp.informatik.tu-darmstadt.de/UKP_Webpag...
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Kurzbeschreibung (Abstract):

We present a method for clustering word senses of a lexical-semantic resource by mapping them to those of another sense inventory. This is a promising way of reducing polysemy in sense inventories and consequently improving word sense disambiguation performance. In contrast to previous approaches, we use Dijkstra-WSA, a parameterizable alignment algorithm which is largely resource- and language-agnostic. To demonstrate this, we apply our technique to GermaNet, the German equivalent to WordNet. The GermaNet sense clusterings we induce through alignments to various collaboratively constructed resources achieve a significant boost in accuracy, even though our method is far less complex and less dependent on language-specific knowledge than past approaches.

Freie Schlagworte: reviewed;UKP_a_LangTech4eHum
ID-Nummer: TUD-CS-2014-0878
Fachbereich(e)/-gebiet(e): 20 Fachbereich Informatik
20 Fachbereich Informatik > Ubiquitäre Wissensverarbeitung
Hinterlegungsdatum: 31 Dez 2016 14:29
Letzte Änderung: 24 Jan 2020 12:03
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