Multilingual Text Mining

Multilingual Text Mining

Peter A. Chew (Sandia National Laboratories, USA)
Copyright: © 2009 |Pages: 6
DOI: 10.4018/978-1-60566-010-3.ch213
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The principles of text mining are fundamental to technology in everyday use. The world wide web (WWW) has in many senses driven research in text mining, and with the growth of the WWW, applications of text mining (like search engines) have by now become commonplace. In a way that was not true even less than a decade ago, it is taken for granted that the ‘needle in the haystack’ can quickly be found among large volumes of text. In most cases, however, users still expect search engines to return results in the same language as that of the query, perhaps the language best understood by the user, or the language in which text is most likely to be available. The distribution of languages on the WWW does not match the distribution of languages spoken in general by the world’s population. For example, while English is spoken by under 10% of the world’s population (Gordon 2005), it is still predominant on the WWW, accounting for perhaps two-thirds of documents. There are variety of possible reasons for this disparity, including technological inequities between different parts of the world and the fact that the WWW had its genesis in an English-speaking country. Whatever the cause for the dominance of English, the fact that two-thirds of the WWW is in one language is, in all likelihood, a major reason that the concept of multilingual text mining is still relatively new. Until recently, there simply has not been a significant and widespread need for multilingual text mining. A number of recent developments have begun to change the situation, however. Perhaps these developments can be grouped under the general rubric of ‘globalization’. They include the increasing adoption, use, and popularization of the WWW in non-Englishspeaking societies; the trend towards political integration of diverse linguistic communities (highly evident, for example, in the European Union); and a growing interest in understanding social, technological and political developments in other parts of the world. All these developments contribute to a greater demand for multilingual text processing – essentially, methods for handling, managing, and comparing documents in multiple languages, some of which may not even be known to the end user.
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A very general and widely-used model for text mining is the vector space model; for a detailed introduction, the reader should consult an information retrieval textbook such as Baeza-Yates & Ribeiro-Neto (1999). Essentially, all variants of the vector space model are based on the insight that documents (or, more generally, chunks of text) can also be thought of as vectors (or columns of a matrix) in which the rows correspond to terms that occur in those documents. The vectors/matrices can be populated by numerical values corresponding to the frequencies of occurrence of particular terms in particular documents, or, more commonly, to weighted frequencies. A variety of weighting schemes are employed; an overview of some of these is given in Dumais (1991). A common practice, before processing, is to eliminate rows in the vectors/matrices corresponding to ‘stopwords’ (Luhn, 1957) – in other words, to ignore from consideration any terms which are considered to be so common that they contribute little to discriminating between documents. At its heart, the vector space model effectively makes the assumption that the meaning of text is an aggregation of the meaning of all the words in the text, and that meaning can be represented in a multidimensional ‘concept space’. Two documents which are similar in meaning will contain many of the same terms, and hence have similar vectors. Furthermore, ‘similarity’ can be quantified using this model; the similarity of two documents in the vector space is the cosine between the vectors for the documents. Document vectors in the vector space model can also be used for supervised predictive mining; an example is in Pang et al. (2002), where document vectors are used to classify movie reviews into ‘positive’ versus ‘negative’.

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