Classifying document titles based on information inference.

Song, Dawei; Bruza, Peter; Huang, Zi; Lau, Raymond Y.K.

Abstract

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).

Citation

SONG, D., BRUZA, P., HUANG, Z. and LAU, R.Y.K. 2003. Classifying document titles based on information inference. In Zhong, N., Ras, Z.W., Tsumoto, S. and Suzuki, E. (eds.) Foundations of intelligent systems: proceedings of the 14th International symposium on methodologies for intelligent systems (ISMIS 2003), 28-31 October 2003, Maebashi City, Japan. Lecture notes in computer science, 2871. Berlin: Springer [online], pages 297-306. Available from: https://doi.org/10.1007/978-3-540-39592-8_41

• Conference Name: 14th International symposium on methodologies for intelligent systems (ISMIS 2003)
• Conference Location: Maebashi City, Japan
• Start Date: Oct 28, 2003
• End Date: Oct 31, 2003
• Acceptance Date: Oct 31, 2003
• Online Publication Date: Oct 31, 2003
• Publication Date: Dec 31, 2003
• Deposit Date: Sep 8, 2009
• Publicly Available Date: Sep 8, 2009
• Publisher: Springer
• Pages: 297-306
• Series Title: Lecture notes in computer science
• Series Number: 2871
• ISBN: 3540202560; 9783540202561
• DOI: https://doi.org/10.1007/978-3-540-39592-8_41
• Keywords: Information inference
• Public URL: http://hdl.handle.net/10059/418