A neural network model for hierarchical multilingual text categorization

  • Authors:

  • Rowena Chau;Chunghsing Yeh;Kate A. Smith

  • Affiliations:

  • School of Business Systems, Faculty of Information Technology, Monash University, Clayton, Victoria, Australia;School of Business Systems, Faculty of Information Technology, Monash University, Clayton, Victoria, Australia;School of Business Systems, Faculty of Information Technology, Monash University, Clayton, Victoria, Australia

  • Venue:
  • ISNN'05 Proceedings of the Second international conference on Advances in neural networks - Volume Part II
  • Year:
  • 2005

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Abstract

Enabling navigation via a hierarchy of conceptually related multilingual documents constitutes the fundamental support to global knowledge discovery. This requirement of organizing multilingual document by concepts makes the goal of supporting global knowledge discovery a concept-based multilingual text categorization task. In this paper, intelligent methods for enabling concept-based hierarchical multilingual text categorization using neural networks are proposed. First, a universal concept space, encapsulating the semantic knowledge of the relationship between all multilingual terms and concepts, which is required by concept-based multilingual text categorization, is generated using a self-organizing map. Second, a set of concept-based multilingual document categories, which acts as the hierarchical backbone of a browseable multilingual document directory, are generated using a hierarchical clustering algorithm. Third, a concept-based multilingual text classifier is developed using a 3-layer feed-forward neural network to facilitate the concept-based multilingual text categorization.