@inproceedings{tay-etal-2018-attentive,
title = "Attentive Gated Lexicon Reader with Contrastive Contextual Co-Attention for Sentiment Classification",
author = "Tay, Yi and
Luu, Anh Tuan and
Hui, Siu Cheung and
Su, Jian",
editor = "Riloff, Ellen and
Chiang, David and
Hockenmaier, Julia and
Tsujii, Jun{'}ichi",
booktitle = "Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing",
month = oct # "-" # nov,
year = "2018",
address = "Brussels, Belgium",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/D18-1381",
doi = "10.18653/v1/D18-1381",
pages = "3443--3453",
abstract = "This paper proposes a new neural architecture that exploits readily available sentiment lexicon resources. The key idea is that that incorporating a word-level prior can aid in the representation learning process, eventually improving model performance. To this end, our model employs two distinctly unique components, i.e., (1) we introduce a lexicon-driven contextual attention mechanism to imbue lexicon words with long-range contextual information and (2), we introduce a contrastive co-attention mechanism that models contrasting polarities between all positive and negative words in a sentence. Via extensive experiments, we show that our approach outperforms many other neural baselines on sentiment classification tasks on multiple benchmark datasets.",
}
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<abstract>This paper proposes a new neural architecture that exploits readily available sentiment lexicon resources. The key idea is that that incorporating a word-level prior can aid in the representation learning process, eventually improving model performance. To this end, our model employs two distinctly unique components, i.e., (1) we introduce a lexicon-driven contextual attention mechanism to imbue lexicon words with long-range contextual information and (2), we introduce a contrastive co-attention mechanism that models contrasting polarities between all positive and negative words in a sentence. Via extensive experiments, we show that our approach outperforms many other neural baselines on sentiment classification tasks on multiple benchmark datasets.</abstract>
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%0 Conference Proceedings
%T Attentive Gated Lexicon Reader with Contrastive Contextual Co-Attention for Sentiment Classification
%A Tay, Yi
%A Luu, Anh Tuan
%A Hui, Siu Cheung
%A Su, Jian
%Y Riloff, Ellen
%Y Chiang, David
%Y Hockenmaier, Julia
%Y Tsujii, Jun’ichi
%S Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing
%D 2018
%8 oct nov
%I Association for Computational Linguistics
%C Brussels, Belgium
%F tay-etal-2018-attentive
%X This paper proposes a new neural architecture that exploits readily available sentiment lexicon resources. The key idea is that that incorporating a word-level prior can aid in the representation learning process, eventually improving model performance. To this end, our model employs two distinctly unique components, i.e., (1) we introduce a lexicon-driven contextual attention mechanism to imbue lexicon words with long-range contextual information and (2), we introduce a contrastive co-attention mechanism that models contrasting polarities between all positive and negative words in a sentence. Via extensive experiments, we show that our approach outperforms many other neural baselines on sentiment classification tasks on multiple benchmark datasets.
%R 10.18653/v1/D18-1381
%U https://aclanthology.org/D18-1381
%U https://doi.org/10.18653/v1/D18-1381
%P 3443-3453
Markdown (Informal)
[Attentive Gated Lexicon Reader with Contrastive Contextual Co-Attention for Sentiment Classification](https://aclanthology.org/D18-1381) (Tay et al., EMNLP 2018)
ACL