Chatting Activity Recognition in Social Occasions Using Factorial Conditional Random Fields with Iterative Classification

Recognizing activities in social occasions plays an important role of building human social networks. For example, the recognition of social interactions could be of great help to determine whether any two attendees have the same interests in an academic conference or a cocktail party. Among the various types of social interactions, chatting with others is a significant indicator. Furthermore, the duration of a chatting activity may imply the strength of the interaction in reality. It is therefore important to recognize the patterns of chatting activities in social occasions. During a real-world conversation, a person often begins talking following the other person’s utterance is completed. Linguistic experts have observed that chatting interaction is usually performed as an interlaced dialogic process. As a result, it is intuitive to apply dynamic probabilistic models to learning and detectin