Language-independent Pre-processing of Large Documentbases for Text Classification (PhD Thesis)
Text classification is a well-known topic in the research of knowledge discovery in databases. Algorithms for text classification generally involve two stages. The first is concerned with identification of textual features (i.e. words and/or phrases) that may be relevant to the classification process. The second is concerned with classification rule mining and categorisation of “unseen” textual data. The first stage is the subject of this thesis and often involves an analysis of text that is both language-specific (and possibly domain-specific), and that may also be computationally costly especially when dealing with large datasets. Existing approaches to this stage are not, therefore, generally applicable to all languages. In this thesis, we examine a number of alternative keyword selection methods and phrase generation strategies, coupled with two potential significant word list construction mechanisms and two final significant word selection mechanisms, to identify such words and/or phrases in a given textual dataset that are expected to serve to distinguish between classes, by simple, language-independent statistical properties. We present experimental results, using common (large) textual datasets presented in two distinct languages, to show that the proposed approaches can produce good performance with respect to both classification accuracy and processing efficiency. In other words, the study presented in this thesis demonstrates the possibility of efficiently solving the traditional text classification problem in a language-independent (also domain-independent) manner.[Full Paper]
For each technical report listed here, copyright and all intellectual property rights remain with the respective authors. Copyright is effective from the year of publication in each case. By downloading a file from this page, you agree to use it only for purposes of research and scholarship. Any other use of this material or storage of it in any medium or its sale or distribution in any form is expressly forbidden without prior written permission from the authors concerned.