Hintz, Gerold ; Biemann, Chris (2016)
Language Transfer Learning for Supervised Lexical Substitution.
Konferenzveröffentlichung, Bibliographie
Kurzbeschreibung (Abstract)
We propose a framework for lexical substitution that is able to perform transfer learning across languages. Datasets for this task are available in at least three languages (English, Italian, and German). Previous work has addressed each of these tasks in isolation. In contrast, we regard the union of three shared tasks as a combined multilingual dataset. We show that a supervised system can be trained effectively, even if training and evaluation data are from different languages. Successful transfer learning between languages suggests that the learned model is in fact independent of the underlying language. We combine state-of-the-art unsupervised features obtained from syntactic word embeddings and distributional thesauri in a supervised delexicalized ranking system. Our system improves over state of the art in the full lexical substitution task in all three languages.
| Typ des Eintrags: | Konferenzveröffentlichung |
|---|---|
| Erschienen: | 2016 |
| Autor(en): | Hintz, Gerold ; Biemann, Chris |
| Art des Eintrags: | Bibliographie |
| Titel: | Language Transfer Learning for Supervised Lexical Substitution |
| Sprache: | Deutsch |
| Publikationsjahr: | August 2016 |
| Buchtitel: | Proceedings of the 54th Annual Meeting of the Association for Computational Linguistics |
| URL / URN: | http://www.aclweb.org/anthology/P16-1012 |
| Zugehörige Links: | |
| Kurzbeschreibung (Abstract): | We propose a framework for lexical substitution that is able to perform transfer learning across languages. Datasets for this task are available in at least three languages (English, Italian, and German). Previous work has addressed each of these tasks in isolation. In contrast, we regard the union of three shared tasks as a combined multilingual dataset. We show that a supervised system can be trained effectively, even if training and evaluation data are from different languages. Successful transfer learning between languages suggests that the learned model is in fact independent of the underlying language. We combine state-of-the-art unsupervised features obtained from syntactic word embeddings and distributional thesauri in a supervised delexicalized ranking system. Our system improves over state of the art in the full lexical substitution task in all three languages. |
| Freie Schlagworte: | AIPHES_area_b3 |
| ID-Nummer: | TUD-CS-2016-0169 |
| Fachbereich(e)/-gebiet(e): | 20 Fachbereich Informatik 20 Fachbereich Informatik > Sprachtechnologie DFG-Graduiertenkollegs DFG-Graduiertenkollegs > Graduiertenkolleg 1994 Adaptive Informationsaufbereitung aus heterogenen Quellen |
| Hinterlegungsdatum: | 30 Dez 2016 17:45 |
| Letzte Änderung: | 28 Sep 2018 15:33 |
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