Injecting Lexical Contrast into Word Vectors by Guiding Vector Space Specialisation

Conference Object
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Vulić, I 
Korhonen, A 

Word vector space specialisation models offer a portable, light-weight approach to fine-tuning arbitrary distributional vector spaces to discern between synonymy and antonymy. Their effectiveness is drawn from external linguistic constraints that specify the exact lexical relation between words. In this work, we show that a careful selection of the external constraints can steer and improve the specialisation. By simply selecting appropriate constraints, we report state-of-the-art results on a suite of tasks with well-defined benchmarks where modeling lexical contrast is crucial: 1) true semantic similarity, with highest reported scores on SimLex-999 and SimVerb-3500 to date; 2) detecting antonyms; and 3) distinguishing antonyms from synonyms.

Journal Title
Proceedings of the Annual Meeting of the Association for Computational Linguistics
Conference Name
Proceedings of the 3rd Workshop on Representation Learning for NLP
Journal ISSN
Volume Title
European Research Council (648909)