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Mild Cognitive Impairment (MCI) is a mental disorder difficult to diagnose.Linguistic features, mainly from parsers, have been used to detect MCI, butthis is not suitable for large-scale assessments. MCI disfluencies producenon-grammatical speech that requires manual or high precision automaticcorrection of transcripts. In this paper, we modeled transcripts into complexnetworks and enriched them with word embedding (CNE) to better represent shorttexts produced in neuropsychological assessments. The network measurements wereapplied with well-known classifiers to automatically identify MCI intranscripts, in a binary classification task. A comparison was made with theperformance of traditional approaches using Bag of Words (BoW) and linguisticfeatures for three datasets: DementiaBank in English, and Cinderella andArizona-Battery in Portuguese. Overall, CNE provided higher accuracy than usingonly complex networks, while Support Vector Machine was superior to otherclassifiers. CNE provided the highest accuracies for DementiaBank andCinderella, but BoW was more efficient for the Arizona-Battery dataset probablyowing to its short narratives. The approach using linguistic features yieldedhigher accuracy if the transcriptions of the Cinderella dataset were manuallyrevised. Taken together, the results indicate that complex networks enrichedwith embedding is promising for detecting MCI in large-scale assessments

Enriching Complex Networks with Word Embeddings for Detecting Mild Cognitive Impairment from Speech Transcripts