Extracting Commonsense Properties from Embeddings with Limited Human Guidance
Abstract
Intelligent systems require common sense, but automatically extracting this knowledge from text can be difficult. We propose and assess methods for extracting one type of commonsense knowledge, object-property comparisons, from pre-trained embeddings. In experiments, we show that our approach exceeds the accuracy of previous work but requires substantially less hand-annotated knowledge. Further, we show that an active learning approach that synthesizes common-sense queries can boost accuracy.- Anthology ID:
- P18-2102
- Volume:
- Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers)
- Month:
- July
- Year:
- 2018
- Address:
- Melbourne, Australia
- Editors:
- Iryna Gurevych, Yusuke Miyao
- Venue:
- ACL
- SIG:
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 644–649
- Language:
- URL:
- https://aclanthology.org/P18-2102
- DOI:
- 10.18653/v1/P18-2102
- Cite (ACL):
- Yiben Yang, Larry Birnbaum, Ji-Ping Wang, and Doug Downey. 2018. Extracting Commonsense Properties from Embeddings with Limited Human Guidance. In Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers), pages 644–649, Melbourne, Australia. Association for Computational Linguistics.
- Cite (Informal):
- Extracting Commonsense Properties from Embeddings with Limited Human Guidance (Yang et al., ACL 2018)
- PDF:
- https://preview.aclanthology.org/teach-a-man-to-fish/P18-2102.pdf
- Code
- yangyiben/PCE