@inproceedings{ruzzetti-etal-2022-lacking,
title = "Lacking the Embedding of a Word? Look it up into a Traditional Dictionary",
author = "Ruzzetti, Elena Sofia and
Ranaldi, Leonardo and
Mastromattei, Michele and
Fallucchi, Francesca and
Scarpato, Noemi and
Zanzotto, Fabio Massimo",
editor = "Muresan, Smaranda and
Nakov, Preslav and
Villavicencio, Aline",
booktitle = "Findings of the Association for Computational Linguistics: ACL 2022",
month = may,
year = "2022",
address = "Dublin, Ireland",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2022.findings-acl.208",
doi = "10.18653/v1/2022.findings-acl.208",
pages = "2651--2662",
abstract = "Word embeddings are powerful dictionaries, which may easily capture language variations. However, these dictionaries fail to give sense to rare words, which are surprisingly often covered by traditional dictionaries. In this paper, we propose to use definitions retrieved in traditional dictionaries to produce word embeddings for rare words. For this purpose, we introduce two methods: Definition Neural Network (DefiNNet) and Define BERT (DefBERT). In our experiments, DefiNNet and DefBERT significantly outperform state-of-the-art as well as baseline methods devised for producing embeddings of unknown words. In fact, DefiNNet significantly outperforms FastText, which implements a method for the same task-based on n-grams, and DefBERT significantly outperforms the BERT method for OOV words. Then, definitions in traditional dictionaries are useful to build word embeddings for rare words.",
}
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<abstract>Word embeddings are powerful dictionaries, which may easily capture language variations. However, these dictionaries fail to give sense to rare words, which are surprisingly often covered by traditional dictionaries. In this paper, we propose to use definitions retrieved in traditional dictionaries to produce word embeddings for rare words. For this purpose, we introduce two methods: Definition Neural Network (DefiNNet) and Define BERT (DefBERT). In our experiments, DefiNNet and DefBERT significantly outperform state-of-the-art as well as baseline methods devised for producing embeddings of unknown words. In fact, DefiNNet significantly outperforms FastText, which implements a method for the same task-based on n-grams, and DefBERT significantly outperforms the BERT method for OOV words. Then, definitions in traditional dictionaries are useful to build word embeddings for rare words.</abstract>
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%0 Conference Proceedings
%T Lacking the Embedding of a Word? Look it up into a Traditional Dictionary
%A Ruzzetti, Elena Sofia
%A Ranaldi, Leonardo
%A Mastromattei, Michele
%A Fallucchi, Francesca
%A Scarpato, Noemi
%A Zanzotto, Fabio Massimo
%Y Muresan, Smaranda
%Y Nakov, Preslav
%Y Villavicencio, Aline
%S Findings of the Association for Computational Linguistics: ACL 2022
%D 2022
%8 May
%I Association for Computational Linguistics
%C Dublin, Ireland
%F ruzzetti-etal-2022-lacking
%X Word embeddings are powerful dictionaries, which may easily capture language variations. However, these dictionaries fail to give sense to rare words, which are surprisingly often covered by traditional dictionaries. In this paper, we propose to use definitions retrieved in traditional dictionaries to produce word embeddings for rare words. For this purpose, we introduce two methods: Definition Neural Network (DefiNNet) and Define BERT (DefBERT). In our experiments, DefiNNet and DefBERT significantly outperform state-of-the-art as well as baseline methods devised for producing embeddings of unknown words. In fact, DefiNNet significantly outperforms FastText, which implements a method for the same task-based on n-grams, and DefBERT significantly outperforms the BERT method for OOV words. Then, definitions in traditional dictionaries are useful to build word embeddings for rare words.
%R 10.18653/v1/2022.findings-acl.208
%U https://aclanthology.org/2022.findings-acl.208
%U https://doi.org/10.18653/v1/2022.findings-acl.208
%P 2651-2662
Markdown (Informal)
[Lacking the Embedding of a Word? Look it up into a Traditional Dictionary](https://aclanthology.org/2022.findings-acl.208) (Ruzzetti et al., Findings 2022)
ACL