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        <title>A Position-aware Bidirectional Attention Network for Aspect-level Sentiment Analysis</title>
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        <namePart type="given">Shuqin</namePart>
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            <title>Proceedings of the 27th International Conference on Computational Linguistics</title>
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            <namePart type="given">Emily</namePart>
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    <abstract>Aspect-level sentiment analysis aims to distinguish the sentiment polarity of each specific aspect term in a given sentence. Both industry and academia have realized the importance of the relationship between aspect term and sentence, and made attempts to model the relationship by designing a series of attention models. However, most existing methods usually neglect the fact that the position information is also crucial for identifying the sentiment polarity of the aspect term. When an aspect term occurs in a sentence, its neighboring words should be given more attention than other words with long distance. Therefore, we propose a position-aware bidirectional attention network (PBAN) based on bidirectional GRU. PBAN not only concentrates on the position information of aspect terms, but also mutually models the relation between aspect term and sentence by employing bidirectional attention mechanism. The experimental results on SemEval 2014 Datasets demonstrate the effectiveness of our proposed PBAN model.</abstract>
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        <date>2018-08</date>
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%0 Conference Proceedings
%T A Position-aware Bidirectional Attention Network for Aspect-level Sentiment Analysis
%A Gu, Shuqin
%A Zhang, Lipeng
%A Hou, Yuexian
%A Song, Yin
%Y Bender, Emily M.
%Y Derczynski, Leon
%Y Isabelle, Pierre
%S Proceedings of the 27th International Conference on Computational Linguistics
%D 2018
%8 August
%I Association for Computational Linguistics
%C Santa Fe, New Mexico, USA
%F gu-etal-2018-position
%X Aspect-level sentiment analysis aims to distinguish the sentiment polarity of each specific aspect term in a given sentence. Both industry and academia have realized the importance of the relationship between aspect term and sentence, and made attempts to model the relationship by designing a series of attention models. However, most existing methods usually neglect the fact that the position information is also crucial for identifying the sentiment polarity of the aspect term. When an aspect term occurs in a sentence, its neighboring words should be given more attention than other words with long distance. Therefore, we propose a position-aware bidirectional attention network (PBAN) based on bidirectional GRU. PBAN not only concentrates on the position information of aspect terms, but also mutually models the relation between aspect term and sentence by employing bidirectional attention mechanism. The experimental results on SemEval 2014 Datasets demonstrate the effectiveness of our proposed PBAN model.
%U https://aclanthology.org/C18-1066
%P 774-784

Markdown (Informal)

[A Position-aware Bidirectional Attention Network for Aspect-level Sentiment Analysis](https://aclanthology.org/C18-1066) (Gu et al., COLING 2018)

• A Position-aware Bidirectional Attention Network for Aspect-level Sentiment Analysis (Gu et al., COLING 2018)

ACL

• Shuqin Gu, Lipeng Zhang, Yuexian Hou, and Yin Song. 2018. A Position-aware Bidirectional Attention Network for Aspect-level Sentiment Analysis. In Proceedings of the 27th International Conference on Computational Linguistics, pages 774–784, Santa Fe, New Mexico, USA. Association for Computational Linguistics.