@InProceedings{chen-zhou-choi:2017:CoNLL,
  author    = {Chen, Henry Y.  and  Zhou, Ethan  and  Choi, Jinho D.},
  title     = {Robust Coreference Resolution and Entity Linking on Dialogues: Character Identification on TV Show Transcripts},
  booktitle = {Proceedings of the 21st Conference on Computational Natural Language Learning (CoNLL 2017)},
  month     = {August},
  year      = {2017},
  address   = {Vancouver, Canada},
  publisher = {Association for Computational Linguistics},
  pages     = {216--225},
  abstract  = {This paper presents a novel approach to character identification, that is an
	entity linking task that maps mentions to characters in dialogues from TV show
	transcripts. We first augment and correct several cases of annotation errors in
	an existing corpus so the corpus is clearer and cleaner for statistical
	learning. We also introduce the agglomerative convolutional neural network that
	takes groups of features and learns mention and mention-pair embeddings for
	coreference resolution. We then propose another neural model that employs the
	embeddings learned and creates cluster embeddings for entity linking. Our
	coreference resolution model shows comparable results to other state-of-the-art
	systems. Our entity linking model significantly outperforms the previous work,
	showing the F1 score of 86.76% and the accuracy of 95.30% for character
	identification.},
  url       = {http://aclweb.org/anthology/K17-1023}
}