Predicting sense convergence with distributional semantics : an application to the CogALex-IV 2014 shared task

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.

View at publisher

Abstract

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.

Impact and interest:

Citation counts are sourced monthly from Scopus and Web of Science® citation databases.

These databases contain citations from different subsets of available publications and different time periods and thus the citation count from each is usually different. Some works are not in either database and no count is displayed. Scopus includes citations from articles published in 1996 onwards, and Web of Science® generally from 1980 onwards.

Citations counts from the Google Scholar™ indexing service can be viewed at the linked Google Scholar™ search.

Full-text downloads:

48 since deposited on 24 Aug 2014
8 in the past twelve months

Full-text downloads displays the total number of times this work’s files (e.g., a PDF) have been downloaded from QUT ePrints as well as the number of downloads in the previous 365 days. The count includes downloads for all files if a work has more than one.

ID Code: 75447
Item Type: Conference Paper
Refereed: Yes
Keywords: CogALex-IV 2014, Computer linguistics, Neural language model
ISBN: 9781873769331
Divisions: Current > Schools > School of Electrical Engineering & Computer Science
Current > QUT Faculties and Divisions > Science & Engineering Faculty
Copyright Owner: Copyright 2014 The Authors
Copyright Statement: The papers in this volume are licensed by the authors under a Creative Commons Attribution 4.0 International License.
Deposited On: 24 Aug 2014 23:56
Last Modified: 26 Aug 2014 07:51

Export: EndNote | Dublin Core | BibTeX

Repository Staff Only: item control page