The AI2 system at SemEval-2017 Task 10 (ScienceIE): semi-supervised end-to-end entity and relation extraction
Waleed Ammar, Matthew E. Peters, Chandra Bhagavatula, Russell Power
Abstract
This paper describes our submission for the ScienceIE shared task (SemEval- 2017 Task 10) on entity and relation extraction from scientific papers. Our model is based on the end-to-end relation extraction model of Miwa and Bansal (2016) with several enhancements such as semi-supervised learning via neural language models, character-level encoding, gazetteers extracted from existing knowledge bases, and model ensembles. Our official submission ranked first in end-to-end entity and relation extraction (scenario 1), and second in the relation-only extraction (scenario 3).- Anthology ID:
- S17-2097
- Volume:
- Proceedings of the 11th International Workshop on Semantic Evaluation (SemEval-2017)
- Month:
- August
- Year:
- 2017
- Address:
- Vancouver, Canada
- Editors:
- Steven Bethard, Marine Carpuat, Marianna Apidianaki, Saif M. Mohammad, Daniel Cer, David Jurgens
- Venue:
- SemEval
- SIG:
- SIGLEX
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 592–596
- Language:
- URL:
- https://aclanthology.org/S17-2097
- DOI:
- 10.18653/v1/S17-2097
- Cite (ACL):
- Waleed Ammar, Matthew E. Peters, Chandra Bhagavatula, and Russell Power. 2017. The AI2 system at SemEval-2017 Task 10 (ScienceIE): semi-supervised end-to-end entity and relation extraction. In Proceedings of the 11th International Workshop on Semantic Evaluation (SemEval-2017), pages 592–596, Vancouver, Canada. Association for Computational Linguistics.
- Cite (Informal):
- The AI2 system at SemEval-2017 Task 10 (ScienceIE): semi-supervised end-to-end entity and relation extraction (Ammar et al., SemEval 2017)
- PDF:
- https://preview.aclanthology.org/nschneid-patch-1/S17-2097.pdf
- Data
- SemEval-2017 Task-10