Enhancing word embeddings with knowledge extracted from lexical resources
Visualitza/Obre
10.18653/v1/2020.acl-srw.36
Inclou dades d'ús des de 2022
Cita com:
hdl:2117/341285
Tipus de documentComunicació de congrés
Data publicació2020
EditorAssociation for Computational Linguistics
Condicions d'accésAccés obert
Llevat que s'hi indiqui el contrari, els
continguts d'aquesta obra estan subjectes a la llicència de Creative Commons
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Reconeixement 4.0 Internacional
Abstract
In this work, we present an effective method for semantic specialization of word vector representations. To this end, we use traditional word embeddings and apply specialization methods to better capture semantic relations between words. In our approach, we leverage external knowledge from rich lexical resources such as BabelNet. We also show that our proposed post-specialization method based on an adversarial neural network with the Wasserstein distance allows to gain improvements over state-of-the-art methods on two tasks: word similarity and dialog state tracking.
CitacióBiesialska, M.; Rafieian, B.; Costa-jussà, M.R. Enhancing word embeddings with knowledge extracted from lexical resources. A: Annual Meeting of the Association for Computational Linguistics. "ACL 2020, The 58th Annual Meeting of the Association for Computational Linguistics: proceedings of the student research workshop: July 5-July 10, 2020". Stroudsburg, PA: Association for Computational Linguistics, 2020, p. 271-278. ISBN 978-1-952148-03-3. DOI 10.18653/v1/2020.acl-srw.36.
ISBN978-1-952148-03-3
Versió de l'editorhttps://www.aclweb.org/anthology/2020.acl-srw.36/
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Enhancing Word Embeddings with.pdf | 434,9Kb | Visualitza/Obre |