Text Classification Using Belief Augmented Frames

: In this paper we present our work on applying Belief Augmented Frames to the text classification problem. We formulate the problem in two alternative ways, and we evaluate the performance of both formulations against established text classification algorithms. We also compare the performance against a text classifier based on Probabilistic Argumentation System, an alternative argumentation system similar to Belief Augmented Frames. We show that Belief Augmented Frames are a promising new approach to text classification, and we present suggestions for future work. Content Areas: Reasoning Systems, Text Analysis, Knowledge Discovery and Data Mining. 

• Type: Conference
• Year: 2004
• Where: PRICAI
• Authors: Colin Keng-Yan Tan
• Keywords: Artificial Intelligence, Belief Augmented Frames, PRICAI 2004, Text Classification, Text Classification Problem