Classifying Document Titles Based on Information Inference

We propose an intelligent document title classification agent based on a theory of information inference. The information is represented as vectorial spaces computed by a cognitively motivated model, namely Hyperspace Analogue to Language (HAL). A combination heuristic is used to combine a group of concepts into one single combination vector. Information inference can be performed on the HAL spaces via computing information flow between vectors or combination vectors. Based on this theory, a document title is treated as a combination vector by applying the combination heuristic to all the non-stop terms in the title. Two methodologies for learning and assigning categories to document titles are addressed. Experimental results on Reuters-21578 corpus show that our framework is promising and its performance achieves 71% of the upper bound (which is approximated by using whole documents).

• Type: Conference
• Year: 2003
• Where: ISMIS
• Authors: Dawei Song, Peter Bruza, Zi Huang, Raymond Y. K. Lau
• Keywords: Combination Vector, Document Titles, Information Inference, ISMIS 2003