Classifying document titles based on information inference.
Bruza, Peter D.
Lau, Raymond Y. K.
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SONG, D., BRUZA, P., HUANG, Z. and LAU, R., 2003. Classifying document titles based on information inference. In: N. ZHONG, Z. RAS, S. TSUMOTO and E. SUZUKI, eds. Foundations of Intelligent Systems : 14th International Symposium, ISMIS 2003 Maebashi City, Japan, October 28-31, 2003 : Proceedings. Berlin: Springer. pp. 297-306.
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).