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- W2891430112 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." @default.
- W2891430112 created "2018-09-27" @default.
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- W2891430112 date "2018-01-01" @default.
- W2891430112 modified "2023-09-27" @default.
- W2891430112 title "Attentive Gated Lexicon Reader with Contrastive Contextual Co-Attention for Sentiment Classification" @default.
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- W2891430112 doi "https://doi.org/10.18653/v1/d18-1381" @default.
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