@inproceedings{liu-chersoni-2022-exploring,
title = "Exploring Nominal Coercion in Semantic Spaces with Static and Contextualized Word Embeddings",
author = "Liu, Chenxin and
Chersoni, Emmanuele",
editor = "Zock, Michael and
Chersoni, Emmanuele and
Hsu, Yu-Yin and
Santus, Enrico",
booktitle = "Proceedings of the Workshop on Cognitive Aspects of the Lexicon",
month = nov,
year = "2022",
address = "Taipei, Taiwan",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2022.cogalex-1.7",
doi = "10.18653/v1/2022.cogalex-1.7",
pages = "49--57",
abstract = "The distinction between mass nouns and count nouns has a long history in formal semantics, and linguists have been trying to identify the semantic properties defining the two classes. However, they also recognized that both can undergo meaning shifts and be used in contexts of a different type, via nominal coercion. In this paper, we present an approach to measure the meaning shift in count-mass coercion in English that makes use of static and contextualized word embedding distance. Our results show that the coercion shifts are detected only by a small subset of the traditional word embedding models, and that the shifts detected by the contextualized embedding of BERT are more pronounced for mass nouns.",
}
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<abstract>The distinction between mass nouns and count nouns has a long history in formal semantics, and linguists have been trying to identify the semantic properties defining the two classes. However, they also recognized that both can undergo meaning shifts and be used in contexts of a different type, via nominal coercion. In this paper, we present an approach to measure the meaning shift in count-mass coercion in English that makes use of static and contextualized word embedding distance. Our results show that the coercion shifts are detected only by a small subset of the traditional word embedding models, and that the shifts detected by the contextualized embedding of BERT are more pronounced for mass nouns.</abstract>
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%0 Conference Proceedings
%T Exploring Nominal Coercion in Semantic Spaces with Static and Contextualized Word Embeddings
%A Liu, Chenxin
%A Chersoni, Emmanuele
%Y Zock, Michael
%Y Chersoni, Emmanuele
%Y Hsu, Yu-Yin
%Y Santus, Enrico
%S Proceedings of the Workshop on Cognitive Aspects of the Lexicon
%D 2022
%8 November
%I Association for Computational Linguistics
%C Taipei, Taiwan
%F liu-chersoni-2022-exploring
%X The distinction between mass nouns and count nouns has a long history in formal semantics, and linguists have been trying to identify the semantic properties defining the two classes. However, they also recognized that both can undergo meaning shifts and be used in contexts of a different type, via nominal coercion. In this paper, we present an approach to measure the meaning shift in count-mass coercion in English that makes use of static and contextualized word embedding distance. Our results show that the coercion shifts are detected only by a small subset of the traditional word embedding models, and that the shifts detected by the contextualized embedding of BERT are more pronounced for mass nouns.
%R 10.18653/v1/2022.cogalex-1.7
%U https://aclanthology.org/2022.cogalex-1.7
%U https://doi.org/10.18653/v1/2022.cogalex-1.7
%P 49-57
Markdown (Informal)
[Exploring Nominal Coercion in Semantic Spaces with Static and Contextualized Word Embeddings](https://aclanthology.org/2022.cogalex-1.7) (Liu & Chersoni, CogALex 2022)
ACL