Researchers in many disciplines have been concerned with modeling textual data in order to account for texts as the primary information unit of written communication. The book ¿Modelling, Learning and Processing of Text-Technological Data Structures¿ deals with this challenging information unit. It focuses on theoretical foundations of representing natural language texts as well as on concrete operations of automatic text processing. Following this integrated approach, the present volume includes contributions to a wide range of topics in the context of processing of textual data. This relates to the learning of ontologies from natural language texts, the annotation and automatic parsing of texts as well as the detection and tracking of topics in texts and hypertexts. In this way, the book brings together a wide range of approaches to procedural aspects of text technology as an emerging scientific discipline.

Focuses on procedural aspects of automatic text analysis

Part I Text Parsing: Data Structures, Architecture and Evaluation.- Part II Measuring Semantic Distance: Methods, Resources, and Applications.- Part III From Textual Data to Ontologies, from Ontologies to Textual Data.- Part IV Multidimensional Representations: Solutions for Complex Markup.- Part V Document Structure Learning.- Part VI Interfacing Textual Data, Ontological Resources and Document Parsing.