Large-scale Opinion Relation Extraction with Distantly Supervised Neural Network

Changzhi Sun, Yuanbin Wu, Man Lan, Shiliang Sun, Qi Zhang


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
Venue:
EACL
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
1033–1043
Language:
URL:
https://aclanthology.org/E17-1097
DOI:
Bibkey:
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)
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PDF:
https://preview.aclanthology.org/ingestion-script-update/E17-1097.pdf