Detecting Mild Cognitive Impairment by Exploiting Linguistic Information from Transcripts

Veronika Vincze, Gábor Gosztolya, László Tóth, Ildikó Hoffmann, Gréta Szatlóczki, Zoltán Bánréti, Magdolna Pákáski, János Kálmán
2016 Proceedings of the 54th Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers)  
Here we seek to automatically identify Hungarian patients suffering from mild cognitive impairment (MCI) based on linguistic features collected from their speech transcripts. Our system uses machine learning techniques and is based on several linguistic features like characteristics of spontaneous speech as well as features exploiting morphological and syntactic parsing. Our results suggest that it is primarily morphological and speechbased features that help distinguish MCI patients from healthy controls.
doi:10.18653/v1/p16-2030 dblp:conf/acl/VinczeGTHSBPK16 fatcat:5ipsgbvduzbvfg6gxl5ek6nfdy