NDLI: Graph-based text classification: learn from your neighbors

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Graph-based text classification: learn from your neighbors

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Graph-based text classification: learn from your neighbors

Content Provider	ACM Digital Library
Author	Weikum, Gerhard Angelova, Ralitsa
Abstract	Automatic classification of data items, based on training samples, can be boosted by considering the neighborhood of data items in a graph structure (e.g., neighboring documents in a hyperlink environment or co-authors and their publications for bibliographic data entries). This paper presents a new method for graph-based classification, with particular emphasis on hyperlinked text documents but broader applicability. Our approach is based on iterative relaxation labeling and can be combined with either Bayesian or SVM classifiers on the feature spaces of the given data items. The graph neighborhood is taken into consideration to exploit locality patterns while at the same time avoiding overfitting. In contrast to prior work along these lines, our approach employs a number of novel techniques: dynamically inferring the link/class pattern in the graph in the run of the iterative relaxation labeling, judicious pruning of edges from the neighborhood graph based on node dissimilarities and node degrees, weighting the influence of edges based on a distance metric between the classification labels of interest and weighting edges by content similarity measures. Our techniques considerably improve the robustness and accuracy of the classification outcome, as shown in systematic experimental comparisons with previously published methods on three different real-world datasets.
Starting Page	485
Ending Page	492
Page Count	8
File Format	PDF
ISBN	1595933697
DOI	10.1145/1148170.1148254
Language	English
Publisher	Association for Computing Machinery (ACM)
Publisher Date	2006-08-06
Publisher Place	New York
Access Restriction	Subscribed
Subject Keyword	Automatic classification Exploiting link structure
Content Type	Text
Resource Type	Article

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