Abstract
This paper presents a system that participated in SemEval 2017 Task 10 (subtask A and subtask B): Extracting Keyphrases and Relations from Scientific Publications (Augenstein et al., 2017). Our proposed approach utilizes external knowledge to enrich feature representation of candidate keyphrase, including Wikipedia, IEEE taxonomy and pre-trained word embeddings etc. Ensemble of unsupervised models, random forest and linear models are used for candidate keyphrase ranking and keyphrase type classification. Our system achieves the 3rd place in subtask A and 4th place in subtask B.- Anthology ID:
- S17-2161
- 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:
- 934–937
- Language:
- URL:
- https://aclanthology.org/S17-2161
- DOI:
- 10.18653/v1/S17-2161
- Cite (ACL):
- Liang Wang and Sujian Li. 2017. PKU_ICL at SemEval-2017 Task 10: Keyphrase Extraction with Model Ensemble and External Knowledge. In Proceedings of the 11th International Workshop on Semantic Evaluation (SemEval-2017), pages 934–937, Vancouver, Canada. Association for Computational Linguistics.
- Cite (Informal):
- PKU_ICL at SemEval-2017 Task 10: Keyphrase Extraction with Model Ensemble and External Knowledge (Wang & Li, SemEval 2017)
- PDF:
- https://preview.aclanthology.org/ingest-acl-2023-videos/S17-2161.pdf
- Data
- SemEval-2017 Task-10