Classifying document titles based on information inference

Dawei Song; Peter Bruza; Zi Huang; Raymond Lau

doi:10.1007/978-3-540-39592-8_41

Classifying document titles based on information inference

Dawei Song, Peter Bruza, Zi Huang, Raymond Lau

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

11 Citations (Scopus)

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 nonstop 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).

Original language	English
Title of host publication	Foundations of Intelligent Systems - 14th International Symposium, ISMIS 2003, Proceedings
Editors	Ning Zhong, Zbigniew W. Ras, Shusaku Tsumoto, Einoshin Suzuki
Publisher	Springer Verlag
Pages	297-306
Number of pages	10
ISBN (Print)	3540202560, 9783540202561
DOIs	https://doi.org/10.1007/978-3-540-39592-8_41
Publication status	Published - 2003
Externally published	Yes
Event	14th International Symposium on Methodologies for Intelligent Systems, ISMIS 2003 - Maebashi City, Japan Duration: 28 Oct 2003 → 31 Oct 2003

Publication series

Name	Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume	2871
ISSN (Print)	0302-9743
ISSN (Electronic)	1611-3349

Conference

Conference	14th International Symposium on Methodologies for Intelligent Systems, ISMIS 2003
Country/Territory	Japan
City	Maebashi City
Period	28/10/03 → 31/10/03

Access to Document

10.1007/978-3-540-39592-8_41

Cite this

Song, D., Bruza, P., Huang, Z., & Lau, R. (2003). Classifying document titles based on information inference. In N. Zhong, Z. W. Ras, S. Tsumoto, & E. Suzuki (Eds.), Foundations of Intelligent Systems - 14th International Symposium, ISMIS 2003, Proceedings (pp. 297-306). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 2871). Springer Verlag. https://doi.org/10.1007/978-3-540-39592-8_41

Song, Dawei ; Bruza, Peter ; Huang, Zi et al. / Classifying document titles based on information inference. Foundations of Intelligent Systems - 14th International Symposium, ISMIS 2003, Proceedings. editor / Ning Zhong ; Zbigniew W. Ras ; Shusaku Tsumoto ; Einoshin Suzuki. Springer Verlag, 2003. pp. 297-306 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)).

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title = "Classifying document titles based on information inference",

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 nonstop 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).",

author = "Dawei Song and Peter Bruza and Zi Huang and Raymond Lau",

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Song, D, Bruza, P, Huang, Z & Lau, R 2003, Classifying document titles based on information inference. in N Zhong, ZW Ras, S Tsumoto & E Suzuki (eds), Foundations of Intelligent Systems - 14th International Symposium, ISMIS 2003, Proceedings. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 2871, Springer Verlag, pp. 297-306, 14th International Symposium on Methodologies for Intelligent Systems, ISMIS 2003, Maebashi City, Japan, 28/10/03. https://doi.org/10.1007/978-3-540-39592-8_41

Classifying document titles based on information inference. / Song, Dawei; Bruza, Peter; Huang, Zi et al.
Foundations of Intelligent Systems - 14th International Symposium, ISMIS 2003, Proceedings. ed. / Ning Zhong; Zbigniew W. Ras; Shusaku Tsumoto; Einoshin Suzuki. Springer Verlag, 2003. p. 297-306 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 2871).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

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T1 - Classifying document titles based on information inference

AU - Song, Dawei

AU - Bruza, Peter

AU - Huang, Zi

AU - Lau, Raymond

N1 - Publisher Copyright: © Springer-Verlag Berlin Heidelberg 2003.

PY - 2003

Y1 - 2003

N2 - 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 nonstop 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).

AB - 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 nonstop 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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T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

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Song D, Bruza P, Huang Z, Lau R. Classifying document titles based on information inference. In Zhong N, Ras ZW, Tsumoto S, Suzuki E, editors, Foundations of Intelligent Systems - 14th International Symposium, ISMIS 2003, Proceedings. Springer Verlag. 2003. p. 297-306. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)). doi: 10.1007/978-3-540-39592-8_41

Classifying document titles based on information inference

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