@InProceedings{treviso-shulby-aluisio:2017:EACLlong,
  author    = {Treviso, Marcos  and  Shulby, Christopher  and  Alu\'{i}sio, Sandra},
  title     = {Sentence Segmentation in Narrative Transcripts from Neuropsychological Tests using Recurrent Convolutional Neural Networks},
  booktitle = {Proceedings of the 15th Conference of the European Chapter of the Association for Computational Linguistics: Volume 1, Long Papers},
  month     = {April},
  year      = {2017},
  address   = {Valencia, Spain},
  publisher = {Association for Computational Linguistics},
  pages     = {315--325},
  abstract  = {Automated discourse analysis tools based on Natural Language Processing (NLP)
	aiming at the diagnosis of language-impairing dementias generally extract
	several textual metrics of narrative transcripts. However, the absence of
	sentence boundary segmentation in the transcripts prevents the direct
	application of NLP methods which rely on these marks in order to function
	properly, such as taggers and parsers. We present the first steps taken towards
	automatic neuropsychological evaluation based on narrative discourse analysis,
	presenting a new automatic sentence segmentation method for impaired speech.
	Our model uses recurrent convolutional neural networks with prosodic, Part of
	Speech (PoS) features, and word embeddings. It was evaluated intrinsically on
	impaired, spontaneous speech as well as normal, prepared speech and presents
	better results for healthy elderly (CTL) (F1 = 0.74) and Mild Cognitive
	Impairment (MCI) patients (F1 = 0.70) than the Conditional Random Fields method
	(F1 = 0.55 and 0.53, respectively) used in the same context of our study. The
	results suggest that our model is robust for impaired speech and can be used in
	automated discourse analysis tools to differentiate narratives produced by MCI
	and CTL.},
  url       = {http://www.aclweb.org/anthology/E17-1030}
}