Detecting deviations in text collections: An approach using conceptual graphs

M. Montes-y-Gómez; A. Gelbukh; A. López-López

doi:10.1007/3-540-46016-0_19

Detecting deviations in text collections: An approach using conceptual graphs

M. Montes-y-Gómez, A. Gelbukh, A. López-López

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

3 Scopus citations

Abstract

Deviation detection is an important problem of both data and text mining. In this paper we consider the detection of deviations in a set of texts represented as conceptual graphs. In contrast with statistical and distance-based approaches, the method we propose is based on the concept of generalization and regularity. Among its main characteristics are the detection of rare patterns (that attempt to give a generalized description of rare texts) and the ability to discover local deviations (deviations at different contexts and generalization levels). The method is illustrated with the analysis of a set of computer science papers.

Original language	English
Title of host publication	MICAI 2002
Subtitle of host publication	Advances in Artificial Intelligence - 2nd Mexican International Conference on Artificial Intelligence, Proceedings
Editors	Carlos A. Coello Coello, Alvaro de Albornoz, Luis Enrique Sucar, Osvaldo Cairo Battistutti
Publisher	Springer Verlag
Pages	176-184
Number of pages	9
ISBN (Print)	3540434755, 9783540434757
DOIs	https://doi.org/10.1007/3-540-46016-0_19
State	Published - 2002
Externally published	Yes
Event	2nd Mexican International Conference on Artificial Intelligence, MICAI 2002 - Merida, Mexico Duration: 22 Apr 2002 → 26 Apr 2002

Publication series

Name	Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume	2313
ISSN (Print)	0302-9743
ISSN (Electronic)	1611-3349

Conference

Conference	2nd Mexican International Conference on Artificial Intelligence, MICAI 2002
Country/Territory	Mexico
City	Merida
Period	22/04/02 → 26/04/02

Keywords

Conceptual graphs
Deviation detection
Natural language processing
Regularity
Text mining

Access to Document

10.1007/3-540-46016-0_19

Cite this

Montes-y-Gómez, M., Gelbukh, A., & López-López, A. (2002). Detecting deviations in text collections: An approach using conceptual graphs. In C. A. Coello Coello, A. de Albornoz, L. E. Sucar, & O. C. Battistutti (Eds.), MICAI 2002: Advances in Artificial Intelligence - 2nd Mexican International Conference on Artificial Intelligence, Proceedings (pp. 176-184). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 2313). Springer Verlag. https://doi.org/10.1007/3-540-46016-0_19

Montes-y-Gómez, M. ; Gelbukh, A. ; López-López, A. / Detecting deviations in text collections : An approach using conceptual graphs. MICAI 2002: Advances in Artificial Intelligence - 2nd Mexican International Conference on Artificial Intelligence, Proceedings. editor / Carlos A. Coello Coello ; Alvaro de Albornoz ; Luis Enrique Sucar ; Osvaldo Cairo Battistutti. Springer Verlag, 2002. pp. 176-184 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)).

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abstract = "Deviation detection is an important problem of both data and text mining. In this paper we consider the detection of deviations in a set of texts represented as conceptual graphs. In contrast with statistical and distance-based approaches, the method we propose is based on the concept of generalization and regularity. Among its main characteristics are the detection of rare patterns (that attempt to give a generalized description of rare texts) and the ability to discover local deviations (deviations at different contexts and generalization levels). The method is illustrated with the analysis of a set of computer science papers.",

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author = "M. Montes-y-G{\'o}mez and A. Gelbukh and A. L{\'o}pez-L{\'o}pez",

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Montes-y-Gómez, M, Gelbukh, A & López-López, A 2002, Detecting deviations in text collections: An approach using conceptual graphs. in CA Coello Coello, A de Albornoz, LE Sucar & OC Battistutti (eds), MICAI 2002: Advances in Artificial Intelligence - 2nd Mexican International Conference on Artificial Intelligence, Proceedings. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 2313, Springer Verlag, pp. 176-184, 2nd Mexican International Conference on Artificial Intelligence, MICAI 2002, Merida, Mexico, 22/04/02. https://doi.org/10.1007/3-540-46016-0_19

Detecting deviations in text collections: An approach using conceptual graphs. / Montes-y-Gómez, M.; Gelbukh, A.; López-López, A.
MICAI 2002: Advances in Artificial Intelligence - 2nd Mexican International Conference on Artificial Intelligence, Proceedings. ed. / Carlos A. Coello Coello; Alvaro de Albornoz; Luis Enrique Sucar; Osvaldo Cairo Battistutti. Springer Verlag, 2002. p. 176-184 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 2313).

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

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T2 - 2nd Mexican International Conference on Artificial Intelligence, MICAI 2002

AU - Montes-y-Gómez, M.

AU - Gelbukh, A.

AU - López-López, A.

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Y1 - 2002

N2 - Deviation detection is an important problem of both data and text mining. In this paper we consider the detection of deviations in a set of texts represented as conceptual graphs. In contrast with statistical and distance-based approaches, the method we propose is based on the concept of generalization and regularity. Among its main characteristics are the detection of rare patterns (that attempt to give a generalized description of rare texts) and the ability to discover local deviations (deviations at different contexts and generalization levels). The method is illustrated with the analysis of a set of computer science papers.

AB - Deviation detection is an important problem of both data and text mining. In this paper we consider the detection of deviations in a set of texts represented as conceptual graphs. In contrast with statistical and distance-based approaches, the method we propose is based on the concept of generalization and regularity. Among its main characteristics are the detection of rare patterns (that attempt to give a generalized description of rare texts) and the ability to discover local deviations (deviations at different contexts and generalization levels). The method is illustrated with the analysis of a set of computer science papers.

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KW - Deviation detection

KW - Natural language processing

KW - Regularity

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SN - 9783540434757

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

SP - 176

EP - 184

BT - MICAI 2002

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A2 - Sucar, Luis Enrique

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Montes-y-Gómez M, Gelbukh A, López-López A. Detecting deviations in text collections: An approach using conceptual graphs. In Coello Coello CA, de Albornoz A, Sucar LE, Battistutti OC, editors, MICAI 2002: Advances in Artificial Intelligence - 2nd Mexican International Conference on Artificial Intelligence, Proceedings. Springer Verlag. 2002. p. 176-184. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)). doi: 10.1007/3-540-46016-0_19

Detecting deviations in text collections: An approach using conceptual graphs

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