Predicting sense convergence with distributional semantics : an application to the CogALex-IV 2014 shared task
| Contribuinte(s) |
Zock, Michael Rapp, Reinhard Huang, Chu-Ren |
|---|---|
| Data(s) |
2014
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| Resumo |
This paper presents our system to address the CogALex-IV 2014 shared task of identifying a single word most semantically related to a group of 5 words (queries). Our system uses an implementation of a neural language model and identifies the answer word by finding the most semantically similar word representation to the sum of the query representations. It is a fully unsupervised system which learns on around 20% of the UkWaC corpus. It correctly identifies 85 exact correct targets out of 2,000 queries, 285 approximate targets in lists of 5 suggestions. |
| Formato |
application/pdf |
| Identificador | |
| Relação |
http://eprints.qut.edu.au/75447/4/75447.pdf http://www.aclweb.org/anthology/W/W14/W14-47.pdf#page=78 Sitbon, Laurianne & De Vine, Lance (2014) Predicting sense convergence with distributional semantics : an application to the CogALex-IV 2014 shared task. In Zock, Michael, Rapp, Reinhard, & Huang, Chu-Ren (Eds.) Proceedings of the 4th Workshop on Cognitive Aspects of the Lexicon, Dublin, Ireland, pp. 64-67. |
| Direitos |
Copyright 2014 The Authors The papers in this volume are licensed by the authors under a Creative Commons Attribution 4.0 International License. |
| Fonte |
School of Electrical Engineering & Computer Science; Science & Engineering Faculty |
| Palavras-Chave | #CogALex-IV 2014 #Computer linguistics #Neural language model |
| Tipo |
Conference Paper |
