Dreams are highly valued in both Freudian psychoanalysis and less conservative clinical traditions. Text mining enables the extraction of meaning from writings in powerful and unexpected ways. In this work, we report methods, uses and results obtained by mining descriptions of dreams. The texts were collected as part of a course in Schizoanalysis (Clinical Psychology) from dozens of participant...

ABSTRACT Text mining and Information Extra tion (IE) are both topi s of signi ant re ent interest. Text mining on erns applying data mining, a.k.a. knowledge dis overy from databases (KDD) te hniques to unstru tured text. Information extra tion (IE) is a form of shallow text understanding that lo ates spe i pie es of data in natural language do uments, transforming unstru tured text into a stru...

The Text mining guided by No Textual data (TNT) is not intended to extract the information contained in the images, aiming the information included in the text that describes these images. In other words, it aims to present to the reader the information about the images next to them, regardless of its real position in the document. Reading, while focusing on no textual data (images, pictures, v...

Across academia and industry, text mining has become a popular strategy for keeping up with the rapid growth of the scientific literature. Text mining of the scientific literature has mostly been carried out on collections of abstracts, due to their availability. Here we present an analysis of 15 million English scientific full-text articles published during the period 1823-2016. We describe th...

There is an urgent need to develop new text mining solutions using High Performance Computing (HPC) and grid environments to tackle the exponential growth in textual data. Problem sizes are increasing by the day by addition of new text documents. Therefore the aim of this work is to lay the foundations for mining large text datasets (i.e. full text articles) in reasonable timeframes. The task o...

Text-mining systems are indispensable tools to reduce the increasing flux of information in scientific literature to topics pertinent to a particular interest in focus. Most of the scientific literature is published as unstructured free text, complicating the development of data processing tools, which rely on structured information. To overcome the problems of free text analysis, structured, h...

Text mining is a new and exciting research area that tries to solve the information overload problem by using techniques from machine learning, natural language processing (NLP), data mining, information retrieval (IR), and knowledge management. Text mining involves the pre-processing of document collections such as information extraction, term extraction, text categorization, and storage of in...

Text mining is about inferring structure from sequences representing natural language text, and may be defined as the process of analyzing text to extract information that is useful for particular purposes. Although hand-crafted heuristics are a common practical approach for extracting information from text, a general, and generalizable, approach requires adaptive techniques. This paper studies...

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