@inproceedings{long-etal-2017-cognition,
title = "A Cognition Based Attention Model for Sentiment Analysis",
author = "Long, Yunfei and
Lu, Qin and
Xiang, Rong and
Li, Minglei and
Huang, Chu-Ren",
editor = "Palmer, Martha and
Hwa, Rebecca and
Riedel, Sebastian",
booktitle = "Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing",
month = sep,
year = "2017",
address = "Copenhagen, Denmark",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/D17-1048",
doi = "10.18653/v1/D17-1048",
pages = "462--471",
abstract = "Attention models are proposed in sentiment analysis because some words are more important than others. However,most existing methods either use local context based text information or user preference information. In this work, we propose a novel attention model trained by cognition grounded eye-tracking data. A reading prediction model is first built using eye-tracking data as dependent data and other features in the context as independent data. The predicted reading time is then used to build a cognition based attention (CBA) layer for neural sentiment analysis. As a comprehensive model, We can capture attentions of words in sentences as well as sentences in documents. Different attention mechanisms can also be incorporated to capture other aspects of attentions. Evaluations show the CBA based method outperforms the state-of-the-art local context based attention methods significantly. This brings insight to how cognition grounded data can be brought into NLP tasks.",
}
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<abstract>Attention models are proposed in sentiment analysis because some words are more important than others. However,most existing methods either use local context based text information or user preference information. In this work, we propose a novel attention model trained by cognition grounded eye-tracking data. A reading prediction model is first built using eye-tracking data as dependent data and other features in the context as independent data. The predicted reading time is then used to build a cognition based attention (CBA) layer for neural sentiment analysis. As a comprehensive model, We can capture attentions of words in sentences as well as sentences in documents. Different attention mechanisms can also be incorporated to capture other aspects of attentions. Evaluations show the CBA based method outperforms the state-of-the-art local context based attention methods significantly. This brings insight to how cognition grounded data can be brought into NLP tasks.</abstract>
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%0 Conference Proceedings
%T A Cognition Based Attention Model for Sentiment Analysis
%A Long, Yunfei
%A Lu, Qin
%A Xiang, Rong
%A Li, Minglei
%A Huang, Chu-Ren
%Y Palmer, Martha
%Y Hwa, Rebecca
%Y Riedel, Sebastian
%S Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing
%D 2017
%8 September
%I Association for Computational Linguistics
%C Copenhagen, Denmark
%F long-etal-2017-cognition
%X Attention models are proposed in sentiment analysis because some words are more important than others. However,most existing methods either use local context based text information or user preference information. In this work, we propose a novel attention model trained by cognition grounded eye-tracking data. A reading prediction model is first built using eye-tracking data as dependent data and other features in the context as independent data. The predicted reading time is then used to build a cognition based attention (CBA) layer for neural sentiment analysis. As a comprehensive model, We can capture attentions of words in sentences as well as sentences in documents. Different attention mechanisms can also be incorporated to capture other aspects of attentions. Evaluations show the CBA based method outperforms the state-of-the-art local context based attention methods significantly. This brings insight to how cognition grounded data can be brought into NLP tasks.
%R 10.18653/v1/D17-1048
%U https://aclanthology.org/D17-1048
%U https://doi.org/10.18653/v1/D17-1048
%P 462-471
Markdown (Informal)
[A Cognition Based Attention Model for Sentiment Analysis](https://aclanthology.org/D17-1048) (Long et al., EMNLP 2017)
ACL
- Yunfei Long, Qin Lu, Rong Xiang, Minglei Li, and Chu-Ren Huang. 2017. A Cognition Based Attention Model for Sentiment Analysis. In Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing, pages 462–471, Copenhagen, Denmark. Association for Computational Linguistics.