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.- Anthology ID:
- 2020.nlpmc-1.9
- Volume:
- Proceedings of the First Workshop on Natural Language Processing for Medical Conversations
- Month:
- July
- Year:
- 2020
- Address:
- Online
- Editors:
- Parminder Bhatia, Steven Lin, Rashmi Gangadharaiah, Byron Wallace, Izhak Shafran, Chaitanya Shivade, Nan Du, Mona Diab
- Venue:
- NLPMC
- SIG:
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 63–67
- Language:
- URL:
- https://aclanthology.org/2020.nlpmc-1.9
- DOI:
- 10.18653/v1/2020.nlpmc-1.9
- Cite (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.
- Cite (Informal):
- Topic-Based Measures of Conversation for Detecting Mild CognitiveImpairment (Asgari et al., NLPMC 2020)
- PDF:
- https://preview.aclanthology.org/nschneid-patch-4/2020.nlpmc-1.9.pdf