Paper: Predicting sense convergence with distributional semantics: an application to the CogaLex 2014 shared task

ACL ID W14-4709
Title Predicting sense convergence with distributional semantics: an application to the CogaLex 2014 shared task
Venue Workshop on Cognitive Aspects of the Lexicon
Session
Year 2014
Authors

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.