Abstract
We investigate the task of open domain opinion relation extraction. Different from works on manually labeled corpus, we propose an efficient distantly supervised framework based on pattern matching and neural network classifiers. The patterns are designed to automatically generate training data, and the deep learning model is design to capture various lexical and syntactic features. The result algorithm is fast and scalable on large-scale corpus. We test the system on the Amazon online review dataset. The result shows that our model is able to achieve promising performances without any human annotations.- Anthology ID:
- E17-1097
- Volume:
- Proceedings of the 15th Conference of the European Chapter of the Association for Computational Linguistics: Volume 1, Long Papers
- Month:
- April
- Year:
- 2017
- Address:
- Valencia, Spain
- Editors:
- Mirella Lapata, Phil Blunsom, Alexander Koller
- Venue:
- EACL
- SIG:
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 1033–1043
- Language:
- URL:
- https://aclanthology.org/E17-1097
- DOI:
- Cite (ACL):
- Changzhi Sun, Yuanbin Wu, Man Lan, Shiliang Sun, and Qi Zhang. 2017. Large-scale Opinion Relation Extraction with Distantly Supervised Neural Network. In Proceedings of the 15th Conference of the European Chapter of the Association for Computational Linguistics: Volume 1, Long Papers, pages 1033–1043, Valencia, Spain. Association for Computational Linguistics.
- Cite (Informal):
- Large-scale Opinion Relation Extraction with Distantly Supervised Neural Network (Sun et al., EACL 2017)
- PDF:
- https://preview.aclanthology.org/ingest-acl-2023-videos/E17-1097.pdf