@InProceedings{wang-EtAl:2016:COLING2,
  author    = {Wang, Yunli  and  Jin, Yong  and  Zhu, Xiaodan  and  Goutte, Cyril},
  title     = {Extracting Discriminative Keyphrases with Learned Semantic Hierarchies},
  booktitle = {Proceedings of COLING 2016, the 26th International Conference on Computational Linguistics: Technical Papers},
  month     = {December},
  year      = {2016},
  address   = {Osaka, Japan},
  publisher = {The COLING 2016 Organizing Committee},
  pages     = {932--942},
  abstract  = {The goal of keyphrase extraction is to automatically identify the most salient
	phrases from documents. The technique has a wide range of applications such as
	rendering a quick glimpse of a document, or extracting key content for further
	use. While previous work often assumes keyphrases are a static property of a
	given documents, in many applications, the appropriate set of keyphrases that
	should be extracted depends on the set of documents that are being considered
	together. In particular, good keyphrases should not only accurately describe
	the content of a document, but also reveal what discriminates it from the other
	documents.
	In this paper, we study this problem of extracting discriminative keyphrases.
	In particularly, we propose to use the hierarchical semantic structure between
	candidate keyphrases to promote keyphrases that have the right level of
	specificity to clearly distinguish the target document from others. We show
	that such knowledge can be used to construct better discriminative keyphrase
	extraction systems that do not assume a static, fixed set of keyphrases for a
	document. We show how this helps identify key expertise of authors from their
	papers, as well as competencies covered by online courses within different
	domains.},
  url       = {http://aclweb.org/anthology/C16-1089}
}