  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
