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        <title>Hierarchical Attention Based Position-Aware Network for Aspect-Level Sentiment Analysis</title>
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        <namePart type="given">Lishuang</namePart>
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    <name type="personal">
        <namePart type="given">AnQiao</namePart>
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            <namePart type="given">Anna</namePart>
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    <abstract>Aspect-level sentiment analysis aims to identify the sentiment of a specific target in its context. Previous works have proved that the interactions between aspects and the contexts are important. On this basis, we also propose a succinct hierarchical attention based mechanism to fuse the information of targets and the contextual words. In addition, most existing methods ignore the position information of the aspect when encoding the sentence. In this paper, we argue that the position-aware representations are beneficial to this task. Therefore, we propose a hierarchical attention based position-aware network (HAPN), which introduces position embeddings to learn the position-aware representations of sentences and further generate the target-specific representations of contextual words. The experimental results on SemEval 2014 dataset show that our approach outperforms the state-of-the-art methods.</abstract>
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    <identifier type="doi">10.18653/v1/K18-1018</identifier>
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%0 Conference Proceedings
%T Hierarchical Attention Based Position-Aware Network for Aspect-Level Sentiment Analysis
%A Li, Lishuang
%A Liu, Yang
%A Zhou, AnQiao
%Y Korhonen, Anna
%Y Titov, Ivan
%S Proceedings of the 22nd Conference on Computational Natural Language Learning
%D 2018
%8 October
%I Association for Computational Linguistics
%C Brussels, Belgium
%F li-etal-2018-hierarchical
%X Aspect-level sentiment analysis aims to identify the sentiment of a specific target in its context. Previous works have proved that the interactions between aspects and the contexts are important. On this basis, we also propose a succinct hierarchical attention based mechanism to fuse the information of targets and the contextual words. In addition, most existing methods ignore the position information of the aspect when encoding the sentence. In this paper, we argue that the position-aware representations are beneficial to this task. Therefore, we propose a hierarchical attention based position-aware network (HAPN), which introduces position embeddings to learn the position-aware representations of sentences and further generate the target-specific representations of contextual words. The experimental results on SemEval 2014 dataset show that our approach outperforms the state-of-the-art methods.
%R 10.18653/v1/K18-1018
%U https://aclanthology.org/K18-1018
%U https://doi.org/10.18653/v1/K18-1018
%P 181-189

Markdown (Informal)

[Hierarchical Attention Based Position-Aware Network for Aspect-Level Sentiment Analysis](https://aclanthology.org/K18-1018) (Li et al., CoNLL 2018)

• Hierarchical Attention Based Position-Aware Network for Aspect-Level Sentiment Analysis (Li et al., CoNLL 2018)

ACL

• Lishuang Li, Yang Liu, and AnQiao Zhou. 2018. Hierarchical Attention Based Position-Aware Network for Aspect-Level Sentiment Analysis. In Proceedings of the 22nd Conference on Computational Natural Language Learning, pages 181–189, Brussels, Belgium. Association for Computational Linguistics.