This book comprises a set of articles that specify the methodology of text mining, describe the creation of lexical resources in the framework of text mining, and use text mining for various tasks in natural language processing (NLP). The analysis of large amounts of textual data is a prerequisite to build lexical resources such as dictionaries and ontologies, and also has direct applications in automated text processing in fields such as history, healthcare and mobile applications, just to name a few. This volume gives an update in terms of the recent gains in text mining methods and reflects the most recent achievements with respect to the automatic build-up of large lexical resources. It addresses researchers that already perform text mining, and those who want to enrich their battery of methods. Selected articles can be used to support graduate-level teaching.
The book is suitable for all readers that completed undergraduate studies of computational linguistics, quantitative linguistics, computer science and computational humanities. It assumes basic knowledge of computer science and corpus processing as well as of statistics.
|
Part I Text Mining Techniques and Methodologies |
|
|
|
Building Large Resources for Text Mining: The Leipzig Corpora Collection |
|
|
3 | (22) |
|
|
|
|
|
|
|
Learning Textologies: Networks of Linked Word Clusters |
|
|
25 | (16) |
|
|
|
Simple, Fast and Accurate Taxonomy Learning |
|
|
41 | (22) |
|
|
|
A Topology-Based Approach to Visualize the Thematic Composition of Document Collections |
|
|
63 | (24) |
|
|
|
|
|
|
|
|
|
Towards a Network Model of the Coreness of Texts: An Experiment in Classifying Latin Texts Using the TTLab Latin Tagger |
|
|
87 | (28) |
|
|
|
|
|
|
|
|
|
Part II Text Mining Applications |
|
|
|
A Structuralist Approach for Personal Knowledge Exploration Systems on Mobile Devices |
|
|
115 | (22) |
|
|
|
|
|
|
|
Natural Language Processing Supporting Interoperability in Healthcare |
|
|
137 | (20) |
|
|
|
|
|
Deception Detection Within and Across Cultures |
|
|
157 | (20) |
|
|
|
|
|
|
|
|
|
Sentiment Analysis: What's Your Opinion? |
|
|
177 | (24) |
|
|
|
|
|
Multi-perspective Event Detection in Texts Documenting the 1944 Battle of Arnhem |
|
|
201 | (20) |
|
|
|
|
|
Towards a Historical Text Re-use Detection |
|
|
221 | |
|
|
|
|
|
|
|
|
|
|
After completing his doctoral dissertation with Gerhard Heyer at the University of Leipzig (Germany), Chris Biemann joined the semantic search startup Powerset (San Francisco) in 2008, which was acquired to become part of Microsoft's Bing in the same year. In 2011, he joined TU Darmstadt (Germany) as an assistant professor (W1) for Language Technology. His interests are situated in statistical semantics, unsupervised and knowledge-free natural language processing and in leveraging the wisdom of the crowds for language data acquisition. Alexander Mehler is professor (W3) for Computational Humanities / Text Technology at the Goethe University Frankfurt am Main, where he heads the Text Technology Lab as part of the Institute of Informatics. His research interests focus on the empirical analysis and simulative synthesis of discourse units in spoken and written communication. He aims at a quantitative theory of networking in linguistic systems to enable multi-agent simulations of their lifecycle. Alexander Mehler integrates models of semantic spaces with simulation models of language evolution and topological models of network theory to capture the complexity of linguistic information systems. Currently, he is heading several research projects on the analysis of linguistic networks in historical semantics. Most recently he started a research project on kinetic text-technologies that integrates the paradigm of games with a purpose with the wiki way of collaborative writing and kinetic HCI.
