2007 AConceptBasedModelForEnhancingTextCateg

• (Shehata et al., 2007) ⇒ Shady Shehata, Fakhri Karray, Mohamed Kamel. (2007). “A Concept-based Model for Enhancing Text Categorization.” In: Proceedings of the 13th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD-2007). doi:10.1145/1281192.1281260

Subject Headings: Supervised Text Classification Algorithm.

Cited By

• ~14 http://scholar.google.com/scholar?cites=6741337391184119570

Quotes

Abstract

• Most of text categorization techniques are based on word and/or phrase analysis of the text. Statistical analysis of a term frequency captures the importance of the term within a document only. However, two terms can have the same frequency in their documents, but one term contributes more to the meaning of its sentences than the other term. Thus, the underlying model should indicate terms that capture these mantics of text. In this case, the model can capture terms that present the concepts of the sentence, which leads to discover the topic of the document. A new concept-based model that analyzes terms on the sentence and document levels rather than the traditional analysis of document only is introduced. The concept-based model can effectively discriminate between non-important terms with respect to sentence semantics and terms which hold the concepts that represent the sentence meaning. The proposed model consists of concept-based statistical analyzer, conceptual ontological graph representation, and concept extractor. The term which contributes to the sentence semantics is assigned two different weights by the concept-based statistical analyzer and the conceptual ontological graph representation. These two weights are combined into a new weight. The concepts that have maximum combined weights are selected by the concept extractor. A set of experiments using the proposed concept-based model on different data sets in text categorization is conducted. The experiments demonstrate the comparison between traditional weighting and the concept-based weighting obtained by the combined approach of the concept-based statistical analyzer and the conceptual ontological graph. The evaluation of results is relied on two quality measures, the Macro-averaged F1 and the Error rate. These quality measures are improved when the newly developed concept-based model is used to enhance the quality of the text categorization.

References

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	Author	volume	Date Value	title	type	journal	titleUrl	doi	note	year
2007 AConceptBasedModelForEnhancingTextCateg	Shady Shehata Fakhri Karray Mohamed Kamel			A Concept-based Model for Enhancing Text Categorization			http://140.123.102.14:8080/reportSys/file/paper/wawa/wawa 6 paper.pdf	10.1145/1281192.1281260