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/ingest-bitext-workshop/S17-2165.pdf