@inproceedings{qian-etal-2017-linguistically,
title = "Linguistically Regularized {LSTM} for Sentiment Classification",
author = "Qian, Qiao and
Huang, Minlie and
Lei, Jinhao and
Zhu, Xiaoyan",
editor = "Barzilay, Regina and
Kan, Min-Yen",
booktitle = "Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)",
month = jul,
year = "2017",
address = "Vancouver, Canada",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/P17-1154",
doi = "10.18653/v1/P17-1154",
pages = "1679--1689",
abstract = "This paper deals with sentence-level sentiment classification. Though a variety of neural network models have been proposed recently, however, previous models either depend on expensive phrase-level annotation, most of which has remarkably degraded performance when trained with only sentence-level annotation; or do not fully employ linguistic resources (e.g., sentiment lexicons, negation words, intensity words). In this paper, we propose simple models trained with sentence-level annotation, but also attempt to model the linguistic role of sentiment lexicons, negation words, and intensity words. Results show that our models are able to capture the linguistic role of sentiment words, negation words, and intensity words in sentiment expression.",
}
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%0 Conference Proceedings
%T Linguistically Regularized LSTM for Sentiment Classification
%A Qian, Qiao
%A Huang, Minlie
%A Lei, Jinhao
%A Zhu, Xiaoyan
%Y Barzilay, Regina
%Y Kan, Min-Yen
%S Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)
%D 2017
%8 July
%I Association for Computational Linguistics
%C Vancouver, Canada
%F qian-etal-2017-linguistically
%X This paper deals with sentence-level sentiment classification. Though a variety of neural network models have been proposed recently, however, previous models either depend on expensive phrase-level annotation, most of which has remarkably degraded performance when trained with only sentence-level annotation; or do not fully employ linguistic resources (e.g., sentiment lexicons, negation words, intensity words). In this paper, we propose simple models trained with sentence-level annotation, but also attempt to model the linguistic role of sentiment lexicons, negation words, and intensity words. Results show that our models are able to capture the linguistic role of sentiment words, negation words, and intensity words in sentiment expression.
%R 10.18653/v1/P17-1154
%U https://aclanthology.org/P17-1154
%U https://doi.org/10.18653/v1/P17-1154
%P 1679-1689
Markdown (Informal)
[Linguistically Regularized LSTM for Sentiment Classification](https://aclanthology.org/P17-1154) (Qian et al., ACL 2017)
ACL
- Qiao Qian, Minlie Huang, Jinhao Lei, and Xiaoyan Zhu. 2017. Linguistically Regularized LSTM for Sentiment Classification. In Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pages 1679–1689, Vancouver, Canada. Association for Computational Linguistics.