CoSimLex: A Resource for Evaluating Graded Word Similarity in Context

Carlos Santos Armendariz, Matthew Purver, Matej Ulčar, Senja Pollak, Nikola Ljubešić, Mark Granroth-Wilding


Abstract
State of the art natural language processing tools are built on context-dependent word embeddings, but no direct method for evaluating these representations currently exists. Standard tasks and datasets for intrinsic evaluation of embeddings are based on judgements of similarity, but ignore context; standard tasks for word sense disambiguation take account of context but do not provide continuous measures of meaning similarity. This paper describes an effort to build a new dataset, CoSimLex, intended to fill this gap. Building on the standard pairwise similarity task of SimLex-999, it provides context-dependent similarity measures; covers not only discrete differences in word sense but more subtle, graded changes in meaning; and covers not only a well-resourced language (English) but a number of less-resourced languages. We define the task and evaluation metrics, outline the dataset collection methodology, and describe the status of the dataset so far.
Anthology ID:
2020.lrec-1.720
Volume:
Proceedings of the 12th Language Resources and Evaluation Conference
Month:
May
Year:
2020
Address:
Marseille, France
Venue:
LREC
SIG:
Publisher:
European Language Resources Association
Note:
Pages:
5878–5886
Language:
English
URL:
https://aclanthology.org/2020.lrec-1.720
DOI:
Bibkey:
Cite (ACL):
Carlos Santos Armendariz, Matthew Purver, Matej Ulčar, Senja Pollak, Nikola Ljubešić, and Mark Granroth-Wilding. 2020. CoSimLex: A Resource for Evaluating Graded Word Similarity in Context. In Proceedings of the 12th Language Resources and Evaluation Conference, pages 5878–5886, Marseille, France. European Language Resources Association.
Cite (Informal):
CoSimLex: A Resource for Evaluating Graded Word Similarity in Context (Armendariz et al., LREC 2020)
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PDF:
https://aclanthology.org/2020.lrec-1.720.pdf