@inproceedings{espinosa-anke-etal-2022-multilingual,
title = "Multilingual Extraction and Categorization of Lexical Collocations with Graph-aware Transformers",
author = "Espinosa Anke, Luis and
Shvets, Alexander and
Mohammadshahi, Alireza and
Henderson, James and
Wanner, Leo",
editor = "Nastase, Vivi and
Pavlick, Ellie and
Pilehvar, Mohammad Taher and
Camacho-Collados, Jose and
Raganato, Alessandro",
booktitle = "Proceedings of the 11th Joint Conference on Lexical and Computational Semantics",
month = jul,
year = "2022",
address = "Seattle, Washington",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2022.starsem-1.8",
doi = "10.18653/v1/2022.starsem-1.8",
pages = "89--100",
abstract = "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.",
}
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%0 Conference Proceedings
%T Multilingual Extraction and Categorization of Lexical Collocations with Graph-aware Transformers
%A Espinosa Anke, Luis
%A Shvets, Alexander
%A Mohammadshahi, Alireza
%A Henderson, James
%A Wanner, Leo
%Y Nastase, Vivi
%Y Pavlick, Ellie
%Y Pilehvar, Mohammad Taher
%Y Camacho-Collados, Jose
%Y Raganato, Alessandro
%S Proceedings of the 11th Joint Conference on Lexical and Computational Semantics
%D 2022
%8 July
%I Association for Computational Linguistics
%C Seattle, Washington
%F espinosa-anke-etal-2022-multilingual
%X 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.
%R 10.18653/v1/2022.starsem-1.8
%U https://aclanthology.org/2022.starsem-1.8
%U https://doi.org/10.18653/v1/2022.starsem-1.8
%P 89-100
Markdown (Informal)
[Multilingual Extraction and Categorization of Lexical Collocations with Graph-aware Transformers](https://aclanthology.org/2022.starsem-1.8) (Espinosa Anke et al., *SEM 2022)
ACL