Aspects of Automatic Text Analysis

• Introduction: Machine Learning in a Semiotic Perspective.- Introduction: Machine Learning in a Semiotic Perspective.- Information Modeling.- Precisiated Natural Language.- On the Issue of Linguistic Approximation.- A Semiotic Approach to Complex Systems.- Models of Semantic Spaces.- On the Mathematics of Semantic Spaces.- Models of Semantic Spaces.- Compositionality in Quantitative Semantics. A Theoretical Perspective on Text Mining.- Quantitative Linguistic Modeling.- A Structuralist Framework for Quantitative Linguistics.- Quantitative Analysis of Syntactic Structures in the Framework of Synergetic Linguistics.- Latent Connotative Text Structure.- Corpus Linguistic and Text Technological Modeling.- Inferring Meaning: Text, Technology and Questions of Induction.- Linguistic Information Modeling: From Kilivila Verb Morphology to RelaxNG.- Affix Discovery by Means of Corpora: Experiments for Spanish, Czech, Ralámuli and Chuj.- Licensing Strategies in Natural Language Processing.- Text Categorization and Classification.- The Surface of Argumentation and the Role of Subordinating Conjunctions.- Computing with Words for Text Categorization.- Neural Networks, Fuzzy Models and Dynamic Logic.- Cognitive Modeling.- A Cognitive Systems Approach to Automatic Text Analysis.- System Theoretical Research on Language and Communication: The Extended Experimental-Simulative Method.- Visual Systems Modeling.- The Dimensionality of Text and Picture and the Cross-Cultural Organization of Semiotic Complexes.

From the reviews: "The book considers recent developments in automatic text analysis, and provides an overview of linguistic modelling ... . As a scientific book from a respected publisher, it naturally is fully referenced at the end of each chapter. The book is of a decent font-size, clear black (not grey!) print, and is easy on the eyes ... . Its intended audience is students and researchers in soft computing and text analysis ... ." (Eric Jukes, Informer, Issue 27, Summer, 2008)