Attentive Gated Lexicon Reader with Contrastive Contextual Co-Attention for Sentiment Classification
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.- Anthology ID:
- D18-1381
- Volume:
- Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing
- Month:
- October-November
- Year:
- 2018
- Address:
- Brussels, Belgium
- Editors:
- Ellen Riloff, David Chiang, Julia Hockenmaier, Jun’ichi Tsujii
- Venue:
- EMNLP
- SIG:
- SIGDAT
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 3443–3453
- Language:
- URL:
- https://aclanthology.org/D18-1381
- DOI:
- 10.18653/v1/D18-1381
- Cite (ACL):
- Yi Tay, Anh Tuan Luu, Siu Cheung Hui, and Jian Su. 2018. Attentive Gated Lexicon Reader with Contrastive Contextual Co-Attention for Sentiment Classification. In Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing, pages 3443–3453, Brussels, Belgium. Association for Computational Linguistics.
- Cite (Informal):
- Attentive Gated Lexicon Reader with Contrastive Contextual Co-Attention for Sentiment Classification (Tay et al., EMNLP 2018)
- PDF:
- https://preview.aclanthology.org/nschneid-patch-2/D18-1381.pdf