Miller, Tristan ; Biemann, Chris ; Zesch, Torsten ; Gurevych, Iryna (2012)
Using Distributional Similarity for Lexical Expansion in Knowledge-based Word Sense Disambiguation.
Mumbai, India
Konferenzveröffentlichung, Bibliographie
Kurzbeschreibung (Abstract)
We explore the contribution of distributional information for purely knowledge-based word sense disambiguation. Specifically, we use a distributional thesaurus, computed from a large parsed corpus, for lexical expansion of context and sense information.This bridges the lexical gap that is seen as the major obstacle for word overlap–based approaches.We apply this mechanism to two traditional knowledge-based methods and show that distributional information significantly improves disambiguation results across several data sets.This improvement exceeds the state of the art for disambiguation without sense frequency information—a situation which is especially encountered with new domains or languages for which no sense-annotated corpus is available.
| Typ des Eintrags: | Konferenzveröffentlichung |
|---|---|
| Erschienen: | 2012 |
| Autor(en): | Miller, Tristan ; Biemann, Chris ; Zesch, Torsten ; Gurevych, Iryna |
| Art des Eintrags: | Bibliographie |
| Titel: | Using Distributional Similarity for Lexical Expansion in Knowledge-based Word Sense Disambiguation |
| Sprache: | Englisch |
| Publikationsjahr: | Dezember 2012 |
| Buchtitel: | Proceedings of the 24th International Conference on Computational Linguistics (COLING 2012) |
| Veranstaltungsort: | Mumbai, India |
| URL / URN: | http://aclweb.org/anthology/C/C12/C12-1109.pdf |
| Kurzbeschreibung (Abstract): | We explore the contribution of distributional information for purely knowledge-based word sense disambiguation. Specifically, we use a distributional thesaurus, computed from a large parsed corpus, for lexical expansion of context and sense information.This bridges the lexical gap that is seen as the major obstacle for word overlap–based approaches.We apply this mechanism to two traditional knowledge-based methods and show that distributional information significantly improves disambiguation results across several data sets.This improvement exceeds the state of the art for disambiguation without sense frequency information—a situation which is especially encountered with new domains or languages for which no sense-annotated corpus is available. |
| Freie Schlagworte: | reviewed;UKP_p_SIDIM;Statistical Semantics;word sense disambiguation, distributional thesaurus, lexical expansion |
| ID-Nummer: | TUD-CS-2012-0232 |
| Fachbereich(e)/-gebiet(e): | 20 Fachbereich Informatik 20 Fachbereich Informatik > Sprachtechnologie 20 Fachbereich Informatik > Ubiquitäre Wissensverarbeitung |
| Hinterlegungsdatum: | 31 Dez 2016 14:29 |
| Letzte Änderung: | 24 Jan 2020 12:03 |
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