UniMa at SemEval-2018 Task 7: Semantic Relation Extraction and Classification from Scientific Publications
Thorsten Keiper, Zhonghao Lyu, Sara Pooladzadeh, Yuan Xu, Jingyi Zhang, Anne Lauscher, Simone Paolo Ponzetto
Abstract
Large repositories of scientific literature call for the development of robust methods to extract information from scholarly papers. This problem is addressed by the SemEval 2018 Task 7 on extracting and classifying relations found within scientific publications. In this paper, we present a feature-based and a deep learning-based approach to the task and discuss the results of the system runs that we submitted for evaluation.- Anthology ID:
- S18-1132
- Volume:
- Proceedings of the 12th International Workshop on Semantic Evaluation
- Month:
- June
- Year:
- 2018
- Address:
- New Orleans, Louisiana
- Editors:
- Marianna Apidianaki, Saif M. Mohammad, Jonathan May, Ekaterina Shutova, Steven Bethard, Marine Carpuat
- Venue:
- SemEval
- SIG:
- SIGLEX
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 826–830
- Language:
- URL:
- https://aclanthology.org/S18-1132
- DOI:
- 10.18653/v1/S18-1132
- Cite (ACL):
- Thorsten Keiper, Zhonghao Lyu, Sara Pooladzadeh, Yuan Xu, Jingyi Zhang, Anne Lauscher, and Simone Paolo Ponzetto. 2018. UniMa at SemEval-2018 Task 7: Semantic Relation Extraction and Classification from Scientific Publications. In Proceedings of the 12th International Workshop on Semantic Evaluation, pages 826–830, New Orleans, Louisiana. Association for Computational Linguistics.
- Cite (Informal):
- UniMa at SemEval-2018 Task 7: Semantic Relation Extraction and Classification from Scientific Publications (Keiper et al., SemEval 2018)
- PDF:
- https://preview.aclanthology.org/landing_page/S18-1132.pdf