Classifying document titles based on information inference

Song, Dawei; Bruza, Peter; Huang, Helen and Lau, Raymond (2003). Classifying document titles based on information inference. In: ed. Foundations of Intelligent Systems. Springer Berlin / Heidelberg, Volume 287 (Volume 287). Springer: Berlin, 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).

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