Modeling, Learning, and Processing of Text-Technological Data Structures

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 Integrates research in the upcoming and challenging text related disciplines. Such as computational linguistics, natural language processing, information retrieval, text and web mining as well as text and language technology Integrates a broad range of methods from text-technology, computational linguistics and machine learning Special emphasis is put on structure learning. Going beyond classical content-related text representation models in information retrieval and computational linguistics

Autorentext
Andreas Witt ist Diplom-Wirtschaftsinformatiker und unter dem Stichwort Web Management als selbstständiger Web-Projekt-Manager tätig. Seit 2004 arbeitet er mit webEdition als Lösungsplattform und engagiert sich als Vorstandsmitglied im webEdition e.V. bei der Weiterentwicklung des Open Source CMS. Dr. Henning Lobin ist am Forschungsbereich 'Document Engineering' an der Fakultät für Linguistik und Literaturwissenschaft der Universität Bielefeld tätig.

Inhalt
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.