Abstract
This paper makes a focused contribution to supervised aspect extraction. It shows that if the system has performed aspect extraction from many past domains and retained their results as knowledge, Conditional Random Fields (CRF) can leverage this knowledge in a lifelong learning manner to extract in a new domain markedly better than the traditional CRF without using this prior knowledge. The key innovation is that even after CRF training, the model can still improve its extraction with experiences in its applications.- Anthology ID:
- P17-2023
- Volume:
- Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers)
- Month:
- July
- Year:
- 2017
- Address:
- Vancouver, Canada
- Editors:
- Regina Barzilay, Min-Yen Kan
- Venue:
- ACL
- SIG:
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 148–154
- Language:
- URL:
- https://aclanthology.org/P17-2023
- DOI:
- 10.18653/v1/P17-2023
- Cite (ACL):
- Lei Shu, Hu Xu, and Bing Liu. 2017. Lifelong Learning CRF for Supervised Aspect Extraction. In Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers), pages 148–154, Vancouver, Canada. Association for Computational Linguistics.
- Cite (Informal):
- Lifelong Learning CRF for Supervised Aspect Extraction (Shu et al., ACL 2017)
- PDF:
- https://preview.aclanthology.org/nschneid-patch-1/P17-2023.pdf