Recursive Neural Structural Correspondence Network for Cross-domain Aspect and Opinion Co-Extraction
Abstract
Fine-grained opinion analysis aims to extract aspect and opinion terms from each sentence for opinion summarization. Supervised learning methods have proven to be effective for this task. However, in many domains, the lack of labeled data hinders the learning of a precise extraction model. In this case, unsupervised domain adaptation methods are desired to transfer knowledge from the source domain to any unlabeled target domain. In this paper, we develop a novel recursive neural network that could reduce domain shift effectively in word level through syntactic relations. We treat these relations as invariant “pivot information” across domains to build structural correspondences and generate an auxiliary task to predict the relation between any two adjacent words in the dependency tree. In the end, we demonstrate state-of-the-art results on three benchmark datasets.- Anthology ID:
- P18-1202
- Volume:
- Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)
- Month:
- July
- Year:
- 2018
- Address:
- Melbourne, Australia
- Editors:
- Iryna Gurevych, Yusuke Miyao
- Venue:
- ACL
- SIG:
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 2171–2181
- Language:
- URL:
- https://aclanthology.org/P18-1202
- DOI:
- 10.18653/v1/P18-1202
- Cite (ACL):
- Wenya Wang and Sinno Jialin Pan. 2018. Recursive Neural Structural Correspondence Network for Cross-domain Aspect and Opinion Co-Extraction. In Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pages 2171–2181, Melbourne, Australia. Association for Computational Linguistics.
- Cite (Informal):
- Recursive Neural Structural Correspondence Network for Cross-domain Aspect and Opinion Co-Extraction (Wang & Pan, ACL 2018)
- PDF:
- https://preview.aclanthology.org/nschneid-patch-3/P18-1202.pdf
- Data
- SemEval-2014 Task-4