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Data Analysis, Machine Learning and Applications
Data analysis and machine learning are research areas at the intersection of computer science, artificial intelligence, mathematics and statistics. They cover general methods and techniques that can be applied to a vast set of applications such as web and text mining, marketing, medical science, bioinformatics and business intelligence. This volume contains the revised versions of selected papers in the field of data analysis, machine learning and applications presented during the 31st Annual Conference of the German Classification Society (Gesellschaft für Klassifikation - GfKl). The conference was held at the Albert-Ludwigs-University in Freiburg, Germany, in March 2007.
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Contributions to the Science of Text and Language
Required reading for computational linguists, this work contains a series of important contributions to the science of language, focusing on the study of word length. The book includes an introduction to the history and state of the art of word length studies. The studies included unify contributions from three crucial fields of study in linguistics: linguistics and text analysis, mathematics and statistics, and corpus and database design. Together they provide a comprehensive approach to the quantitative study of text and language.
Aggregating Dialectology, Typology, and Register Analysis
This volume aims to overcome sub-disciplinary boundaries in the study of linguistic variation - be it language-internal or cross-linguistic. Even though dialectologists, register analysts, typologists, and quantitative linguists all deal with linguistic variation, there is astonishingly little interaction across these fields. But the fourteen contributions in this volume show that these subdisciplines actually share many interests and methodological concerns in common. The chapters specifically converge in the following ways: First, they all seek to explore linguistic variation, within or across languages. Second, they are based on usage data, that is, on corpora of (more or less) authentic ...
