Abstract
We introduce a novel parameterized convolutional neural network for aspect level sentiment classification. Using parameterized filters and parameterized gates, we incorporate aspect information into convolutional neural networks (CNN). Experiments demonstrate that our parameterized filters and parameterized gates effectively capture the aspect-specific features, and our CNN-based models achieve excellent results on SemEval 2014 datasets.- Anthology ID:
- D18-1136
- Volume:
- Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing
- Month:
- October-November
- Year:
- 2018
- Address:
- Brussels, Belgium
- Venue:
- EMNLP
- SIG:
- SIGDAT
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 1091–1096
- Language:
- URL:
- https://aclanthology.org/D18-1136
- DOI:
- 10.18653/v1/D18-1136
- Cite (ACL):
- Binxuan Huang and Kathleen Carley. 2018. Parameterized Convolutional Neural Networks for Aspect Level Sentiment Classification. In Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing, pages 1091–1096, Brussels, Belgium. Association for Computational Linguistics.
- Cite (Informal):
- Parameterized Convolutional Neural Networks for Aspect Level Sentiment Classification (Huang & Carley, EMNLP 2018)
- PDF:
- https://preview.aclanthology.org/auto-file-uploads/D18-1136.pdf
- Data
- SemEval 2014 Task 4 Sub Task 2