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.
CITATION STYLE
Wang, L., & Li, S. (2017). PKU ICL at SemEval-2017 Task 10: Keyphrase Extraction with Model Ensemble and External Knowledge. In Proceedings of the Annual Meeting of the Association for Computational Linguistics (pp. 934–937). Association for Computational Linguistics (ACL). https://doi.org/10.18653/v1/s17-2161
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