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Document Indexing Techniques for Text Mining
Abstract
Owing to the growing amount of digital information stored in natural language, systems that automatically process text are of crucial importance and extremely useful. There is currently a considerable amount of research work (Sebastiani, 2002; Crammer et al., 2003) using a large variety of machine learning algorithms and other Knowledge Discovery in Databases (KDD) methods that are applied to Text Categorization (automatically labeling of texts according to category), Information Retrieval (retrieval of texts similar to a given cue), Information Extraction (identification of pieces of text that contains certain meanings), and Question/Answering (automatic answering of user questions about a certain topic). The texts or documents used can be stored either in ad hoc databases or in the World Wide Web. Data mining in texts, the well-known Text Mining, is a case of KDD with some particular issues: on one hand, the features are obtained from the words contained in texts or are the words themselves. Therefore, text mining systems faces with a huge amount of attributes. On the other hand, the features are highly correlated to form meanings, so it is necessary to take the relationships among words into account, what implies the consideration of syntax and semantics as human beings do. KDD techniques require input texts to be represented as a set of attributes in order to deal with them. The text-to-representation process is called text or document indexing, and the attributes and called indexes. Accordingly, indexing is a crucial process in text mining because indexed representations must collect, only with a set of indexes, most of the information expressed in natural language in the texts with the minimum loss of semantics, in order to perform as well as possible.
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