Sciweavers

Share
TIST
2011

Probabilistic models for concurrent chatting activity recognition

9 years 9 months ago
Probabilistic models for concurrent chatting activity recognition
Recognition of chatting activities in social interactions is useful for constructing human social networks. However, the existence of multiple people involved in multiple dialogues presents special challenges. To model the conversational dynamics of concurrent chatting behaviors, this paper advocates Factorial Conditional Random Fields (FCRFs) as a model to accommodate co-temporal relationships among multiple activity states. In addition, to avoid the use of inefficient Loopy Belief Propagation (LBP) algorithm, we propose using Iterative Classification Algorithm (ICA) as the inference method for FCRFs. We designed experiments to compare our FCRFs model with two dynamic probabilistic models, Parallel Condition Random Fields (PCRFs) and Hidden Markov Models (HMMs), in learning and decoding based on auditory data. The experimental results show that FCRFs outperform PCRFs and HMM-like models. We also discover that FCRFs using the ICA inference approach not only improves the recognition ...
Jane Yung-jen Hsu, Chia-chun Lian, Wan-rong Jih
Added 15 May 2011
Updated 15 May 2011
Type Journal
Year 2011
Where TIST
Authors Jane Yung-jen Hsu, Chia-chun Lian, Wan-rong Jih
Comments (0)
books