Maximal Margin Labeling for Multi-Topic Text Categorization

Hideto Kazawa; Tomonori Izumitani; Hirotoshi Taira; Eisaku Maeda

Abstract

In this paper, we address the problem of statistical learning for multitopic text categorization (MTC), whose goal is to choose all relevant topics (a label) from a given set of topics. The proposed algorithm, Maximal Margin Labeling (MML), treats all possible labels as independent classes and learns a multi-class classifier on the induced multi-class categorization problem. To cope with the data sparseness caused by the huge number of possible labels, MML combines some prior knowledge about label prototypes and a maximal margin criterion in a novel way. Experiments with multi-topic Web pages show that MML outperforms existing learning algorithms including Support Vector Machines. 1 Multi-topic Text Categorization (MTC) This paper addresses the problem of learning for multi-topic text categorization (MTC), whose goal is to select all topics relevant to a text from a given set of topics. In MTC, multiple topics may be relevant to a single text. We thus call a set of topics label, and say

Keywords

CategorizationText categorizationMargin (machine learning)Computer scienceArtificial intelligenceMachine learningClassifier (UML)Class (philosophy)Support vector machineSet (abstract data type)Natural language processingPattern recognition (psychology)

Affiliated Institutions

NTT (Japan) JP

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Publication Info

Year: 2004
Type: article
Volume: 17
Pages: 649-656
Citations: 113
Access: Closed

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APA Style

                            
                                    Hideto Kazawa, 
                                
                                    Tomonori Izumitani, 
                                
                                    Hirotoshi Taira
                                
                                et al.
                            
                            (2004). 
                            Maximal Margin Labeling for Multi-Topic Text Categorization. 
                            
                            , 17
                            
                            , 649-656.