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

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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
Bibkey:
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)
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PDF:
https://preview.aclanthology.org/teach-a-man-to-fish/S18-1132.pdf
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