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|Title: ||Classifying document titles based on information inference.|
|Authors: ||Song, Dawei|
Bruza, Peter D.
Lau, Raymond Y. K.
|Editors: ||Zhong, N.|
Ras, Zbigniew W.
|Keywords: ||Information inference|
|Issue Date: ||Oct-2003|
|Citation: ||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.
|Series/Report no.: ||Lecture Notes in Artificial Intelligence|
|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).|
|Appears in Collections:||Conference publications (Computing)|
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