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        <title>Collocation Classification with Unsupervised Relation Vectors</title>
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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.</abstract>
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%0 Conference Proceedings
%T Collocation Classification with Unsupervised Relation Vectors
%A Espinosa Anke, Luis
%A Schockaert, Steven
%A Wanner, Leo
%Y Korhonen, Anna
%Y Traum, David
%Y Màrquez, Lluís
%S Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics
%D 2019
%8 July
%I Association for Computational Linguistics
%C Florence, Italy
%F espinosa-anke-etal-2019-collocation
%X 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.
%R 10.18653/v1/P19-1576
%U https://aclanthology.org/P19-1576
%U https://doi.org/10.18653/v1/P19-1576
%P 5765-5772

Markdown (Informal)

[Collocation Classification with Unsupervised Relation Vectors](https://aclanthology.org/P19-1576) (Espinosa Anke et al., ACL 2019)

• Collocation Classification with Unsupervised Relation Vectors (Espinosa Anke et al., ACL 2019)

ACL

• Luis Espinosa Anke, Steven Schockaert, and Leo Wanner. 2019. Collocation Classification with Unsupervised Relation Vectors. In Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics, pages 5765–5772, Florence, Italy. Association for Computational Linguistics.