Feature Reinforcement Approach to Poly-lingual Text Categorization

With the rapid emergence and proliferation of Internet and the trend of globalization, a tremendous amount of textual documents written in different languages are electronically accessible online. Poly-lingual text categorization (PLTC) refers to the automatic learning of a text categorization model(s) from a set of preclassified training documents written in different languages and the subsequent assignment of unclassified poly-lingual documents to predefined categories on the basis of the induced text categorization model(s). Although PLTC can be approached as multiple independent monolingual text categorization problems, this naïve approach employs only the training documents of the same language to construct a monolingual classifier and fails to utilize the opportunity offered by poly-lingual training documents. In this study, we propose a feature reinforcement approach to PLTC that takes into account the training documents of all languages when constructing a monolingual classifier for a specific language. Using the independent monolingual text categorization (MnTC) technique as performance benchmarks, our empirical evaluation results show that the proposed PLTC technique achieves higher classification accuracy than the benchmark technique does in both English and Chinese corpora.