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
Venue:
SemEval
SIGs:
SIGLEX | SIGSEM
Publisher:
Association for Computational Linguistics
Note:
Pages:
592–596
Language:
URL:
https://aclanthology.org/S17-2097
DOI:
10.18653/v1/S17-2097
Bibkey:
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)
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PDF:
https://aclanthology.org/S17-2097.pdf