The NTNU System at SemEval-2017 Task 10: Extracting Keyphrases and Relations from Scientific Publications Using Multiple Conditional Random Fields
Abstract
This study describes the design of the NTNU system for the ScienceIE task at the SemEval 2017 workshop. We use self-defined feature templates and multiple conditional random fields with extracted features to identify keyphrases along with categorized labels and their relations from scientific publications. A total of 16 teams participated in evaluation scenario 1 (subtasks A, B, and C), with only 7 teams competing in all sub-tasks. Our best micro-averaging F1 across the three subtasks is 0.23, ranking in the middle among all 16 submissions.- Anthology ID:
- S17-2165
- 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:
- 951–955
- Language:
- URL:
- https://aclanthology.org/S17-2165
- DOI:
- 10.18653/v1/S17-2165
- Cite (ACL):
- Lung-Hao Lee, Kuei-Ching Lee, and Yuen-Hsien Tseng. 2017. The NTNU System at SemEval-2017 Task 10: Extracting Keyphrases and Relations from Scientific Publications Using Multiple Conditional Random Fields. In Proceedings of the 11th International Workshop on Semantic Evaluation (SemEval-2017), pages 951–955, Vancouver, Canada. Association for Computational Linguistics.
- Cite (Informal):
- The NTNU System at SemEval-2017 Task 10: Extracting Keyphrases and Relations from Scientific Publications Using Multiple Conditional Random Fields (Lee et al., SemEval 2017)
- PDF:
- https://preview.aclanthology.org/teach-a-man-to-fish/S17-2165.pdf