@InProceedings{prasad-kan:2017:SemEval,
  author    = {Prasad, Animesh  and  Kan, Min-Yen},
  title     = {WING-NUS at SemEval-2017 Task 10: Keyphrase Extraction and Classification as Joint Sequence Labeling},
  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     = {973--977},
  abstract  = {We describe an end-to-end pipeline processing approach for SemEval
	2017's Task 10 to extract keyphrases and their relations from
	scientific publications.  We jointly identify and classify keyphrases
	by modeling the subtasks as sequential labeling.  Our system utilizes
	standard, surface-level features along with the adjacent word
	features, and performs conditional decoding on whole text to extract
	keyphrases.  
	We focus only on the identification and typing of keyphrases (Subtasks
	A and B, together referred as extraction), but provide an end-to-end system
	inclusive of keyphrase
	relation identification (Subtask C) for completeness.  Our top
	performing configuration achieves an $F\_1$ of 0.27 for the end-to-end
	keyphrase extraction and relation identification scenario on the final test
	data, and
	compares on par to other top ranked systems for keyphrase extraction.  Our
	system outperforms other techniques that do not employ global decoding
	and hence do not account for dependencies between keyphrases. We
	believe this is crucial for keyphrase classification in the given
	context of scientific document mining.},
  url       = {http://www.aclweb.org/anthology/S17-2170}
}