@InProceedings{basaldella-chiaradia-tasso:2016:COLING,
  author    = {Basaldella, Marco  and  Chiaradia, Giorgia  and  Tasso, Carlo},
  title     = {Evaluating anaphora and coreference resolution to improve automatic keyphrase extraction},
  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     = {804--814},
  abstract  = {In this paper we analyze the effectiveness of using linguistic knowledge from
	coreference and anaphora resolution for improving the performance for
	supervised keyphrase extraction. 
	In order to verify the impact of these features, we define a baseline 
	keyphrase extraction system and evaluate its performance on a standard dataset
	using different machine learning algorithms. 
	Then, we consider new sets of features by adding combinations of the linguistic
	features we propose and we evaluate the new performance of the system. We also
	use anaphora and coreference resolution to transform the documents, trying to
	simulate the cohesion process performed by the human mind. 
	We found that our approach has a slightly positive impact on the performance of
	automatic keyphrase extraction, in particular when considering the ranking of
	the results.},
  url       = {http://aclweb.org/anthology/C16-1077}
}