Topic-based measures of conversation for detecting mild cognitive impairment

Liu Chen, Hiroko H. Dodge, Meysam Asgari

Research output: Chapter in Book/Report/Conference proceedingConference contribution

8 Scopus citations

Abstract

Conversation is a complex cognitive task that engages multiple aspects of cognitive functions to remember the discussed topics, monitor the semantic and linguistic elements, and recognize others’ emotions. In this paper, we propose a computational method based on the lexical coherence of consecutive utterances to quantify topical variations in semi-structured conversations of older adults with cognitive impairments. Extracting the lexical knowledge of conversational utterances, our method generates a set of novel conversational measures that indicate underlying cognitive deficits among subjects with mild cognitive impairment (MCI). Our preliminary results verify the utility of the proposed conversation-based measures in distinguishing MCI from healthy controls.

Original languageEnglish (US)
Title of host publicationACL 2020 - Natural Language Processing for Medical Conversations, Proceedings of the Workshop
PublisherAssociation for Computational Linguistics (ACL)
Pages63-67
Number of pages5
ISBN (Electronic)9781952148200
StatePublished - 2020
Event2020 Workshop on Natural Language Processing for Medical Conversations, NLPMC 2020 at the 58th Annual Meeting of the Association for Computational Linguistics, ACL 2020 - Virtual, Online, United States
Duration: Jul 10 2020 → …

Publication series

NameProceedings of the Annual Meeting of the Association for Computational Linguistics
ISSN (Print)0736-587X

Conference

Conference2020 Workshop on Natural Language Processing for Medical Conversations, NLPMC 2020 at the 58th Annual Meeting of the Association for Computational Linguistics, ACL 2020
Country/TerritoryUnited States
CityVirtual, Online
Period7/10/20 → …

ASJC Scopus subject areas

  • Computer Science Applications
  • Linguistics and Language
  • Language and Linguistics

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