Espinosa-Anke, Luis  ORCID: https://orcid.org/0000-0001-6830-9176, Schockaert, Steven ORCID: https://orcid.org/0000-0002-9256-2881 and Wanner, Leo
2019.
Collocation classification with unsupervised relation vectors.
Presented at: 57th Annual Meeting of the Association for Computational Linguistics (ACL),
Florence, Italy,
28 July - 2 August 2019.
Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics.
Association for Computational Linguistics,
pp. 5765-5772.
10.18653/v1/P19-1576
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Abstract
Lexical relation classification is the task of predicting whether a certain relation holds between a given pair of words. In this paper, we explore to which extent the current distributional landscape based on word embeddings provides a suitable basis for classification of collocations, i.e., pairs of words between which idiosyncratic lexical relations hold. First, we introduce a novel dataset with collocations categorized according to lexical functions. Second, we conduct experiments on a subset of this benchmark, comparing it in particular to the well known DiffVec dataset. In these experiments, in addition to simple word vector arithmetic operations, we also investigate the role of unsupervised relation vectors as a complementary input. While these relation vectors indeed help, we also show that lexical function classification poses a greater challenge than the syntactic and semantic relations that are typically used for benchmarks in the literature.
| Item Type: | Conference or Workshop Item (Paper) |
|---|---|
| Date Type: | Publication |
| Status: | Published |
| Schools: | Schools > Computer Science & Informatics |
| Publisher: | Association for Computational Linguistics |
| ISBN: | 9781950737482 |
| Date of First Compliant Deposit: | 14 August 2019 |
| Last Modified: | 04 Sep 2025 10:45 |
| URI: | https://orca.cardiff.ac.uk/id/eprint/124038 |
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