A method for multilingual text mining and retrieval using growing hierarchical self-organizing maps

With the increasing number of multilingual texts in the internet, multilingual text retrieval techniques have become an important research issue. However, the discovery of relationships between different languages remains an open problem. In this paper we propose a method, which applies the growing...

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Bibliographic Details
Published in:Journal of information science 2009-02, Vol.35 (1), p.3-23
Main Authors: Yang, Hsin-Chang, Lee, Chung-Hong, Chen, Ding-Wen
Format: Article
Language:English
Subjects:
Data mining
Exact sciences and technology
Feature maps
Information and communication sciences
Information retrieval
Information retrieval systems
Information retrieval systems. Information and document management system
Information science. Documentation
Languages
Multilingual systems
Multilingualism
Online information retrieval
Sciences and techniques of general use
Self organizing maps
Studies
Texts
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Summary:With the increasing number of multilingual texts in the internet, multilingual text retrieval techniques have become an important research issue. However, the discovery of relationships between different languages remains an open problem. In this paper we propose a method, which applies the growing hierarchical self-organizing map (GHSOM) model, to discover knowledge from multilingual text documents. Multilingual parallel corpora were trained by the GHSOM to generate hierarchical feature maps. A discovery process is then applied on these maps to discover the relationships between documents of different languages. The relationships between keywords of different languages are also revealed. We conducted experiments on a set of Chinese—English bilingual parallel corpora to discover the relationships between documents of these languages. We also use such relationships to perform multilingual information retrieval tasks. The experimental results show that our multilingual text mining approach may capture conceptual relationships among documents as well as keywords written in different languages.
ISSN:0165-5515
1741-6485
DOI:10.1177/0165551508088968