Multilingual Extraction and Categorization of Lexical Collocations with Graph-aware Transformers
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Abstract (arXiv)
Recognizing and categorizing lexical collocations in context is useful for language learning, dictionary compilation and downstream NLP. However, it is a challenging task due to the varying degrees of frozenness lexical collocations exhibit. In this paper, we put forward a sequence tagging BERT-based model enhanced with a graph-aware transformer architecture, which we evaluate on the task of collocation recognition in context. Our results suggest that explicitly encoding syntactic dependencies in the model architecture is helpful, and provide insights on differences in collocation typification in English, Spanish and French.
Key facts. Cited by 2 papers in the map (data through 14 Oct 2026). Builds on 15 of its 76 references that are in the map. First posted on arXiv 23 May 2022.
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15 of 76 references.
References · 15
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Citing papers per year: 2023: 2.
2 papers.
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- 20233D-EX : A Unified Dataset of Definitions and Dictionary Examplescited by 1
- 2023Transformers as Graph-to-Graph Models27 Oct 2023
- 20233D-EX : A Unified Dataset of Definitions and Dictionary Examples6 Aug 2023