TY - GEN
T1 - Detecting Health Related Discussions in Everyday Telephone Conversations for Studying Medical Events in the Lives of Older Adults
AU - Sheikhshab, Golnar
AU - Shafran, Izhak
AU - Kaye, Jeffrey
N1 - Publisher Copyright:
©2014 Association for Computational Linguistics
PY - 2014
Y1 - 2014
N2 - We apply semi-supervised topic modeling techniques to detect health-related discussions in everyday telephone conversations, which has applications in large-scale epidemiological studies and for clinical interventions for older adults. The privacy requirements associated with utilizing everyday telephone conversations preclude manual annotations; hence, we explore semi-supervised methods in this task. We adopt a semi-supervised version of Latent Dirichlet Allocation (LDA) to guide the learning process. Within this framework, we investigate a strategy to discard irrelevant words in the topic distribution and demonstrate that this strategy improves the average F-score on the in-domain task and an out-of-domain task (Fisher corpus). Our results show that the increase in discussion of health related conversations is statistically associated with actual medical events obtained through weekly self-reports.
AB - We apply semi-supervised topic modeling techniques to detect health-related discussions in everyday telephone conversations, which has applications in large-scale epidemiological studies and for clinical interventions for older adults. The privacy requirements associated with utilizing everyday telephone conversations preclude manual annotations; hence, we explore semi-supervised methods in this task. We adopt a semi-supervised version of Latent Dirichlet Allocation (LDA) to guide the learning process. Within this framework, we investigate a strategy to discard irrelevant words in the topic distribution and demonstrate that this strategy improves the average F-score on the in-domain task and an out-of-domain task (Fisher corpus). Our results show that the increase in discussion of health related conversations is statistically associated with actual medical events obtained through weekly self-reports.
UR - http://www.scopus.com/inward/record.url?scp=85122512963&partnerID=8YFLogxK
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M3 - Conference contribution
AN - SCOPUS:85122512963
T3 - Proceedings of the Annual Meeting of the Association for Computational Linguistics
SP - 38
EP - 44
BT - ACL 2014 - BioNLP 2014, Workshop on Biomedical Natural Language Processing, Proceedings of the Workshop
PB - Association for Computational Linguistics (ACL)
T2 - ACL 2014 Workshop on Biomedical Natural Language Processing, BioNLP 2014
Y2 - 27 June 2014 through 28 June 2014
ER -