@inproceedings{gari-soler-etal-2019-word,
title = "Word Usage Similarity Estimation with Sentence Representations and Automatic Substitutes",
author = "Gar{\'\i} Soler, Aina and
Apidianaki, Marianna and
Allauzen, Alexandre",
editor = "Mihalcea, Rada and
Shutova, Ekaterina and
Ku, Lun-Wei and
Evang, Kilian and
Poria, Soujanya",
booktitle = "Proceedings of the Eighth Joint Conference on Lexical and Computational Semantics (*{SEM} 2019)",
month = jun,
year = "2019",
address = "Minneapolis, Minnesota",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/S19-1002",
doi = "10.18653/v1/S19-1002",
pages = "9--21",
abstract = "Usage similarity estimation addresses the semantic proximity of word instances in different contexts. We apply contextualized (ELMo and BERT) word and sentence embeddings to this task, and propose supervised models that leverage these representations for prediction. Our models are further assisted by lexical substitute annotations automatically assigned to word instances by context2vec, a neural model that relies on a bidirectional LSTM. We perform an extensive comparison of existing word and sentence representations on benchmark datasets addressing both graded and binary similarity. The best performing models outperform previous methods in both settings.",
}
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<abstract>Usage similarity estimation addresses the semantic proximity of word instances in different contexts. We apply contextualized (ELMo and BERT) word and sentence embeddings to this task, and propose supervised models that leverage these representations for prediction. Our models are further assisted by lexical substitute annotations automatically assigned to word instances by context2vec, a neural model that relies on a bidirectional LSTM. We perform an extensive comparison of existing word and sentence representations on benchmark datasets addressing both graded and binary similarity. The best performing models outperform previous methods in both settings.</abstract>
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%0 Conference Proceedings
%T Word Usage Similarity Estimation with Sentence Representations and Automatic Substitutes
%A Garí Soler, Aina
%A Apidianaki, Marianna
%A Allauzen, Alexandre
%Y Mihalcea, Rada
%Y Shutova, Ekaterina
%Y Ku, Lun-Wei
%Y Evang, Kilian
%Y Poria, Soujanya
%S Proceedings of the Eighth Joint Conference on Lexical and Computational Semantics (*SEM 2019)
%D 2019
%8 June
%I Association for Computational Linguistics
%C Minneapolis, Minnesota
%F gari-soler-etal-2019-word
%X Usage similarity estimation addresses the semantic proximity of word instances in different contexts. We apply contextualized (ELMo and BERT) word and sentence embeddings to this task, and propose supervised models that leverage these representations for prediction. Our models are further assisted by lexical substitute annotations automatically assigned to word instances by context2vec, a neural model that relies on a bidirectional LSTM. We perform an extensive comparison of existing word and sentence representations on benchmark datasets addressing both graded and binary similarity. The best performing models outperform previous methods in both settings.
%R 10.18653/v1/S19-1002
%U https://aclanthology.org/S19-1002
%U https://doi.org/10.18653/v1/S19-1002
%P 9-21
Markdown (Informal)
[Word Usage Similarity Estimation with Sentence Representations and Automatic Substitutes](https://aclanthology.org/S19-1002) (Garí Soler et al., *SEM 2019)
ACL