@inproceedings{ferret-2024-language-models,
title = "Language Models and Semantic Relations: A Dual Relationship",
author = "Ferret, Olivier",
editor = "Calzolari, Nicoletta and
Kan, Min-Yen and
Hoste, Veronique and
Lenci, Alessandro and
Sakti, Sakriani and
Xue, Nianwen",
booktitle = "Proceedings of the 2024 Joint International Conference on Computational Linguistics, Language Resources and Evaluation (LREC-COLING 2024)",
month = may,
year = "2024",
address = "Torino, Italia",
publisher = "ELRA and ICCL",
url = "https://aclanthology.org/2024.lrec-main.878",
pages = "10046--10057",
abstract = "Since they rely on the distributional hypothesis, static and contextual language models are closely linked to lexical semantic relations. In this paper, we exploit this link for enhancing a BERT model. More precisely, we propose to extract lexical semantic relations with two unsupervised methods, one based on a static language model, the other on a contextual model, and to inject the extracted relations into a BERT model for improving its semantic capabilities. Through various evaluations performed for English and focusing on semantic similarity at the word and sentence levels, we show the interest of this approach, allowing us to semantically enrich a BERT model without using any external semantic resource.",
}
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<abstract>Since they rely on the distributional hypothesis, static and contextual language models are closely linked to lexical semantic relations. In this paper, we exploit this link for enhancing a BERT model. More precisely, we propose to extract lexical semantic relations with two unsupervised methods, one based on a static language model, the other on a contextual model, and to inject the extracted relations into a BERT model for improving its semantic capabilities. Through various evaluations performed for English and focusing on semantic similarity at the word and sentence levels, we show the interest of this approach, allowing us to semantically enrich a BERT model without using any external semantic resource.</abstract>
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%0 Conference Proceedings
%T Language Models and Semantic Relations: A Dual Relationship
%A Ferret, Olivier
%Y Calzolari, Nicoletta
%Y Kan, Min-Yen
%Y Hoste, Veronique
%Y Lenci, Alessandro
%Y Sakti, Sakriani
%Y Xue, Nianwen
%S Proceedings of the 2024 Joint International Conference on Computational Linguistics, Language Resources and Evaluation (LREC-COLING 2024)
%D 2024
%8 May
%I ELRA and ICCL
%C Torino, Italia
%F ferret-2024-language-models
%X Since they rely on the distributional hypothesis, static and contextual language models are closely linked to lexical semantic relations. In this paper, we exploit this link for enhancing a BERT model. More precisely, we propose to extract lexical semantic relations with two unsupervised methods, one based on a static language model, the other on a contextual model, and to inject the extracted relations into a BERT model for improving its semantic capabilities. Through various evaluations performed for English and focusing on semantic similarity at the word and sentence levels, we show the interest of this approach, allowing us to semantically enrich a BERT model without using any external semantic resource.
%U https://aclanthology.org/2024.lrec-main.878
%P 10046-10057
Markdown (Informal)
[Language Models and Semantic Relations: A Dual Relationship](https://aclanthology.org/2024.lrec-main.878) (Ferret, LREC-COLING 2024)
ACL