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    <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 generate a set of novel conversational measures that indicate underlying cognitive deficits among subjects with mild cognitive impairment (MCI). Our preliminary results verifies the utility of the proposed conversation-based measures in distinguishing MCI from healthy controls.</abstract>
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%0 Conference Proceedings
%T Topic-Based Measures of Conversation for Detecting Mild CognitiveImpairment
%A Asgari, Meysam
%A Chen, Liu
%A Dodge, Hiroko
%Y Bhatia, Parminder
%Y Lin, Steven
%Y Gangadharaiah, Rashmi
%Y Wallace, Byron
%Y Shafran, Izhak
%Y Shivade, Chaitanya
%Y Du, Nan
%Y Diab, Mona
%S Proceedings of the First Workshop on Natural Language Processing for Medical Conversations
%D 2020
%8 July
%I Association for Computational Linguistics
%C Online
%F asgari-etal-2020-topic
%X 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 generate a set of novel conversational measures that indicate underlying cognitive deficits among subjects with mild cognitive impairment (MCI). Our preliminary results verifies the utility of the proposed conversation-based measures in distinguishing MCI from healthy controls.
%R 10.18653/v1/2020.nlpmc-1.9
%U https://aclanthology.org/2020.nlpmc-1.9/
%U https://doi.org/10.18653/v1/2020.nlpmc-1.9
%P 63-67

Markdown (Informal)

[Topic-Based Measures of Conversation for Detecting Mild CognitiveImpairment](https://aclanthology.org/2020.nlpmc-1.9/) (Asgari et al., NLPMC 2020)

• Topic-Based Measures of Conversation for Detecting Mild CognitiveImpairment (Asgari et al., NLPMC 2020)

ACL

• Meysam Asgari, Liu Chen, and Hiroko Dodge. 2020. Topic-Based Measures of Conversation for Detecting Mild CognitiveImpairment. In Proceedings of the First Workshop on Natural Language Processing for Medical Conversations, pages 63–67, Online. Association for Computational Linguistics.