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        <title>Effective Attention Modeling for Aspect-Level Sentiment Classification</title>
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        <namePart type="given">Ruidan</namePart>
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    <abstract>Aspect-level sentiment classification aims to determine the sentiment polarity of a review sentence towards an opinion target. A sentence could contain multiple sentiment-target pairs; thus the main challenge of this task is to separate different opinion contexts for different targets. To this end, attention mechanism has played an important role in previous state-of-the-art neural models. The mechanism is able to capture the importance of each context word towards a target by modeling their semantic associations. We build upon this line of research and propose two novel approaches for improving the effectiveness of attention. First, we propose a method for target representation that better captures the semantic meaning of the opinion target. Second, we introduce an attention model that incorporates syntactic information into the attention mechanism. We experiment on attention-based LSTM (Long Short-Term Memory) models using the datasets from SemEval 2014, 2015, and 2016. The experimental results show that the conventional attention-based LSTM can be substantially improved by incorporating the two approaches.</abstract>
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        <date>2018-08</date>
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%0 Conference Proceedings
%T Effective Attention Modeling for Aspect-Level Sentiment Classification
%A He, Ruidan
%A Lee, Wee Sun
%A Ng, Hwee Tou
%A Dahlmeier, Daniel
%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 he-etal-2018-effective
%X Aspect-level sentiment classification aims to determine the sentiment polarity of a review sentence towards an opinion target. A sentence could contain multiple sentiment-target pairs; thus the main challenge of this task is to separate different opinion contexts for different targets. To this end, attention mechanism has played an important role in previous state-of-the-art neural models. The mechanism is able to capture the importance of each context word towards a target by modeling their semantic associations. We build upon this line of research and propose two novel approaches for improving the effectiveness of attention. First, we propose a method for target representation that better captures the semantic meaning of the opinion target. Second, we introduce an attention model that incorporates syntactic information into the attention mechanism. We experiment on attention-based LSTM (Long Short-Term Memory) models using the datasets from SemEval 2014, 2015, and 2016. The experimental results show that the conventional attention-based LSTM can be substantially improved by incorporating the two approaches.
%U https://aclanthology.org/C18-1096
%P 1121-1131

Markdown (Informal)

[Effective Attention Modeling for Aspect-Level Sentiment Classification](https://aclanthology.org/C18-1096) (He et al., COLING 2018)

• Effective Attention Modeling for Aspect-Level Sentiment Classification (He et al., COLING 2018)

ACL

• Ruidan He, Wee Sun Lee, Hwee Tou Ng, and Daniel Dahlmeier. 2018. Effective Attention Modeling for Aspect-Level Sentiment Classification. In Proceedings of the 27th International Conference on Computational Linguistics, pages 1121–1131, Santa Fe, New Mexico, USA. Association for Computational Linguistics.