Paper: Predicting Subjectivity in Multimodal Conversations

ACL ID D09-1140
Title Predicting Subjectivity in Multimodal Conversations
Venue Conference on Empirical Methods in Natural Language Processing
Session Main Conference
Year 2009

In this research we aim to detect sub- jective sentences in multimodal conversa- tions. We introduce a novel technique wherein subjective patterns are learned from both labeled and unlabeled data, us- ing n-gram word sequences with vary- ing levels of lexical instantiation. Ap- plying this technique to meeting speech and email conversations, we gain signifi- cant improvement over state-of-the-art ap- proaches. Furthermore, we show that cou- pling the pattern-based approach with fea- tures that capture characteristics of gen- eral conversation structure yields addi- tional improvement.