@inproceedings{akama-etal-2018-unsupervised,
title = "Unsupervised Learning of Style-sensitive Word Vectors",
author = "Akama, Reina and
Watanabe, Kento and
Yokoi, Sho and
Kobayashi, Sosuke and
Inui, Kentaro",
editor = "Gurevych, Iryna and
Miyao, Yusuke",
booktitle = "Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers)",
month = jul,
year = "2018",
address = "Melbourne, Australia",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/P18-2091",
doi = "10.18653/v1/P18-2091",
pages = "572--578",
abstract = "This paper presents the first study aimed at capturing stylistic similarity between words in an unsupervised manner. We propose extending the continuous bag of words (CBOW) embedding model (Mikolov et al., 2013b) to learn style-sensitive word vectors using a wider context window under the assumption that the style of all the words in an utterance is consistent. In addition, we introduce a novel task to predict lexical stylistic similarity and to create a benchmark dataset for this task. Our experiment with this dataset supports our assumption and demonstrates that the proposed extensions contribute to the acquisition of style-sensitive word embeddings.",
}
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%0 Conference Proceedings
%T Unsupervised Learning of Style-sensitive Word Vectors
%A Akama, Reina
%A Watanabe, Kento
%A Yokoi, Sho
%A Kobayashi, Sosuke
%A Inui, Kentaro
%Y Gurevych, Iryna
%Y Miyao, Yusuke
%S Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers)
%D 2018
%8 July
%I Association for Computational Linguistics
%C Melbourne, Australia
%F akama-etal-2018-unsupervised
%X This paper presents the first study aimed at capturing stylistic similarity between words in an unsupervised manner. We propose extending the continuous bag of words (CBOW) embedding model (Mikolov et al., 2013b) to learn style-sensitive word vectors using a wider context window under the assumption that the style of all the words in an utterance is consistent. In addition, we introduce a novel task to predict lexical stylistic similarity and to create a benchmark dataset for this task. Our experiment with this dataset supports our assumption and demonstrates that the proposed extensions contribute to the acquisition of style-sensitive word embeddings.
%R 10.18653/v1/P18-2091
%U https://aclanthology.org/P18-2091
%U https://doi.org/10.18653/v1/P18-2091
%P 572-578
Markdown (Informal)
[Unsupervised Learning of Style-sensitive Word Vectors](https://aclanthology.org/P18-2091) (Akama et al., ACL 2018)
ACL
- Reina Akama, Kento Watanabe, Sho Yokoi, Sosuke Kobayashi, and Kentaro Inui. 2018. Unsupervised Learning of Style-sensitive Word Vectors. In Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers), pages 572–578, Melbourne, Australia. Association for Computational Linguistics.