The exponential growth of the internet has led to a great deal of interest in developing useful and efficient tools and software to assist users in searching the Web. Document retrieval, categorization, routing and filtering can all be formulated as classification problems. However, the complexity of natural languages and the extremely high dimensionality of the feature space of documents have ...

Text mining is the process of obtaining useful and interesting information from text. Huge amount of text data is available in the form of various formats. Most of it is unstructured.Text mining usually involves the process of structuring the input text which involves parsing it, structuring it by inserting results into a database, deriving patterns from the structured data, and finally evaluat...

These days textual information becomes increasingly available through the Web. This makes text an attractive resource from which to mine knowledge. The major difficulty in mining textual data is that the information is unstructured. Hence the data has to be preprocessed first so as to obtain some form of structured data which is amenable to data mining techniques. This paper focuses on this pre...

It is a traditional belief that in order to scale-up to more effective retrieval and access methods modern Information Retrieval has to consider more the text content. The modalities and techniques to fit this objectives are still under discussion. More empirical evidence is required to determine the suitable linguistic levels for modeling each IR subtask (e.g. information zoning, parsing, feat...

Automatic text classification (ATC) is a discipline at the crossroads of information retrieval (IR), machine learning (ML), and computational linguistics (CL), and consists in the realization of text classifiers, i.e. software systems capable of assigning texts to one or more categories, or classes, from a predefined set. Applications range from the automated indexing of scientific articles, to...