@InProceedings{liu-EtAl:2017:SemEval2,
  author    = {Liu, Sijia  and  Shen, Feichen  and  Chaudhary, Vipin  and  Liu, Hongfang},
  title     = {MayoNLP at SemEval 2017 Task 10: Word Embedding Distance Pattern for Keyphrase Classification in Scientific Publications},
  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     = {956--960},
  abstract  = {In this paper, we present MayoNLP's results from the participation in the
	ScienceIE share task at SemEval 2017. We focused on the keyphrase
	classification task (Subtask B). We explored 
	semantic similarities and patterns of keyphrases in scientific publications
	using pre-trained word embedding models. Word Embedding Distance Pattern, which
	uses the head noun word embedding to generate distance patterns based on
	labeled keyphrases, is proposed as an incremental feature set to enhance the
	conventional Named Entity Recognition feature sets.  Support vector machine is
	used as the supervised classifier for keyphrase classification.
	Our system achieved an overall F1 score of 0.67 for keyphrase classification
	and 0.64 for keyphrase classification and relation detection.},
  url       = {http://www.aclweb.org/anthology/S17-2166}
}