@InProceedings{wang-li:2017:SemEval,
  author    = {Wang, Liang  and  Li, Sujian},
  title     = {PKU\_ICL at SemEval-2017 Task 10: Keyphrase Extraction with Model Ensemble and External Knowledge},
  booktitle = {Proceedings of the 11th International Workshop on Semantic Evaluation (SemEval-2017)},
  month     = {August},
  year      = {2017},
  address   = {Vancouver, Canada},
  publisher = {Association for Computational Linguistics},
  pages     = {934--937},
  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.},
  url       = {http://www.aclweb.org/anthology/S17-2161}
}