Chatting Activity Recognition in Social Occasions Using Factorial Conditional Random Fields with Iterative Classification
نویسندگان
چکیده
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 detecting chatting activities.
منابع مشابه
Joint Recognition of Multiple Concurrent Activities using Factorial Conditional Random Fields
Recognizing patterns of human activities is an important enabling technology for building intelligent home environments. Existing approaches to activity recognition often focus on mutually exclusive activities only. In reality, people routinely carry out multiple concurrent activities. It is therefore necessary to model the co-temporal relationships among activities. In this paper, we propose u...
متن کاملConditional Random Fields for Airborne Lidar Point Cloud Classification in Urban Area
Over the past decades, urban growth has been known as a worldwide phenomenon that includes widening process and expanding pattern. While the cities are changing rapidly, their quantitative analysis as well as decision making in urban planning can benefit from two-dimensional (2D) and three-dimensional (3D) digital models. The recent developments in imaging and non-imaging sensor technologies, s...
متن کاملA Novel Approach to Conditional Random Field-based Named Entity Recognition using Persian Specific Features
Named Entity Recognition is an information extraction technique that identifies name entities in a text. Three popular methods have been conventionally used namely: rule-based, machine-learning-based and hybrid of them to extract named entities from a text. Machine-learning-based methods have good performance in the Persian language if they are trained with good features. To get good performanc...
متن کاملAutomatic Prosodic Labeling with Conditional Random Fields and Rich Acoustic Features
Many acoustic approaches to prosodic labeling in English have employed only local classifiers, although text-based classification has employed some sequential models. In this paper we employ linear chain and factorial conditional random fields (CRFs) in conjunction with rich, contextually-based prosodic features, to exploit sequential dependencies and to facilitate integration with lexical feat...
متن کاملStrategies for Inference Mechanism of Conditional Random Fields for Multiple-Resident Activity Recognition in a Smart Home
Multiple-resident activity recognition is a major challenge for building a smart-home system. In this paper, conditional random fields (CRFs) are chosen as our activity recognition models for overcoming this challenge. We evaluate our proposed approach with several strategies, including conditional random field with iterative inference and the one with decomposition inference, to enhance the co...
متن کامل