Abstract
While neural networks have been shown to achieve impressive results for sentence-level sentiment analysis, targeted aspect-based sentiment analysis (TABSA) — extraction of fine-grained opinion polarity w.r.t. a pre-defined set of aspects — remains a difficult task. Motivated by recent advances in memory-augmented models for machine reading, we propose a novel architecture, utilising external “memory chains” with a delayed memory update mechanism to track entities. On a TABSA task, the proposed model demonstrates substantial improvements over state-of-the-art approaches, including those using external knowledge bases.- Anthology ID:
- N18-2045
- Volume:
- Proceedings of the 2018 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 2 (Short Papers)
- Month:
- June
- Year:
- 2018
- Address:
- New Orleans, Louisiana
- Editors:
- Marilyn Walker, Heng Ji, Amanda Stent
- Venue:
- NAACL
- SIG:
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 278–283
- Language:
- URL:
- https://aclanthology.org/N18-2045
- DOI:
- 10.18653/v1/N18-2045
- Cite (ACL):
- Fei Liu, Trevor Cohn, and Timothy Baldwin. 2018. Recurrent Entity Networks with Delayed Memory Update for Targeted Aspect-Based Sentiment Analysis. In Proceedings of the 2018 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 2 (Short Papers), pages 278–283, New Orleans, Louisiana. Association for Computational Linguistics.
- Cite (Informal):
- Recurrent Entity Networks with Delayed Memory Update for Targeted Aspect-Based Sentiment Analysis (Liu et al., NAACL 2018)
- PDF:
- https://preview.aclanthology.org/nschneid-patch-2/N18-2045.pdf
- Code
- liufly/delayed-memory-update-entnet
- Data
- CBT