Paper: Detecting Health Related Discussions in Everyday Telephone Conversations for Studying Medical Events in the Lives of Older Adults

ACL ID W14-3406
Title Detecting Health Related Discussions in Everyday Telephone Conversations for Studying Medical Events in the Lives of Older Adults
Venue Proceedings of the BioNLP Shared Task 2013 Workshop
Session
Year 2014
Authors

We apply semi-supervised topic modeling techniques to detect health-related discus- sions in everyday telephone conversations, which has applications in large-scale epi- demiological studies and for clinical in- terventions for older adults. The privacy requirements associated with utilizing ev- eryday 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 irrel- evant 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 dis- cussion...