Counter-fitting Word Vectors to Linguistic Constraints

Autor:	Mrkšić, Nikola, Séaghdha, Diarmuid Ó, Thomson, Blaise, Gašić, Milica, Rojas-Barahona, Lina, Su, Pei-Hao, Vandyke, David, Wen, Tsung-Hsien, Young, Steve
Rok vydání:	2016
Předmět:	Computer Science - Computation and Language Computer Science - Learning
Druh dokumentu:	Working Paper
Popis:	In this work, we present a novel counter-fitting method which injects antonymy and synonymy constraints into vector space representations in order to improve the vectors' capability for judging semantic similarity. Applying this method to publicly available pre-trained word vectors leads to a new state of the art performance on the SimLex-999 dataset. We also show how the method can be used to tailor the word vector space for the downstream task of dialogue state tracking, resulting in robust improvements across different dialogue domains. Comment: Paper accepted for the 15th Annual Conference of the North American Chapter of the Association for Computational Linguistics (NAACL 2016)
Databáze:	arXiv
Externí odkaz:	http://arxiv.org/abs/1603.00892 Zobrazit plný text záznamu View this record from Arxiv