@InProceedings{santos-EtAl:2017:Long,
  author    = {Santos, Leandro  and  Corr\^{e}a J\'{u}nior, Edilson Anselmo  and  Oliveira Jr, Osvaldo  and  Amancio, Diego  and  Mansur, Let\'{i}cia  and  Alu\'{i}sio, Sandra},
  title     = {Enriching Complex Networks with Word Embeddings for Detecting Mild Cognitive Impairment from Speech Transcripts},
  booktitle = {Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)},
  month     = {July},
  year      = {2017},
  address   = {Vancouver, Canada},
  publisher = {Association for Computational Linguistics},
  pages     = {1284--1296},
  abstract  = {Mild Cognitive Impairment (MCI) is a mental disorder difficult to diagnose.
	Linguistic features, mainly from parsers, have been used to detect MCI, but
	this is not suitable for large-scale assessments. MCI disfluencies produce
	non-grammatical speech that requires manual or high precision automatic
	correction of transcripts.  In this paper, we modeled transcripts into complex
	networks and enriched them with word embedding (CNE) to better represent short
	texts produced in neuropsychological assessments. The network measurements were
	applied with well-known classifiers to automatically identify MCI in
	transcripts, in a binary classification task. A comparison was made with the
	performance of traditional approaches using Bag of Words (BoW) and linguistic
	features for three datasets: DementiaBank in English, and Cinderella and
	Arizona-Battery in Portuguese. Overall, CNE provided higher accuracy than using
	only complex networks, while Support Vector Machine was superior to other
	classifiers. CNE provided the highest accuracies for DementiaBank and
	Cinderella, but BoW was more efficient for the Arizona-Battery dataset probably
	owing to its short narratives. The approach using linguistic features yielded
	higher accuracy if the transcriptions of the Cinderella dataset were manually
	revised. Taken together, the results indicate that complex networks enriched
	with embedding is promising for detecting MCI in large-scale assessments.},
  url       = {http://aclweb.org/anthology/P17-1118}
}