Cluster Analysis for Corpus Linguistics

The standard scientific methodology in linguistics is empirical testing of falsifiable hypotheses. As such the process of hypothesis generation is central, and involves formulation of a research question about a domain of interest and statement of a hypothesis relative to it. In corpus linguistics the domain is text, and generation involves abstraction of data from text, data analysis, and formulation of a hypothesis based on inference from the results. Traditionally this process has been paper-based, but the advent of electronic text has increasingly rendered it obsolete both because the size of digital corpora is now at or beyond the limit of what can efficiently be used in the traditional way, and because the complexity of data abstracted from them can be impenetrable to understanding. Linguists are increasingly turning to mathematical and statistical computational methods for help, and cluster analysis is such a method. It is used across the sciences for hypothesis generation by identification of structure in data which are too large or complex, or both, to be interpretable by direct inspection. This book aims to show how cluster analysis can be used for hypothesis generation in corpus linguistics, thereby contributing to a quantitative empirical methodology for the discipline. * Describes a range of clustering methods for analysis of data derived from language corpora. * Gives an intuitively accessible account of the mathematical concepts which underlie data creation, data transformation, and cluster analysis.

The standard scientific methodology in linguistics is empirical testing of falsifiable hypotheses. As such the process of hypothesis generation is central, and involves formulation of a research question about a domain of interest and statement of a hypothesis relative to it. In corpus linguistics the domain is text, and generation involves abstraction of data from text, data analysis, and formulation of a hypothesis based on inference from the results. Traditionally this process has been paper-based, but the advent of electronic text has increasingly rendered it obsolete both because the size of digital corpora is now at or beyond the limit of what can efficiently be used in the traditional way, and because the complexity of data abstracted from them can be impenetrable to understanding. Linguists are increasingly turning to mathematical and statistical computational methods for help, and cluster analysis is such a method. It is used across the sciences for hypothesis generation by identification of structure in data which are too large or complex, or both, to be interpretable by direct inspection. This book aims to show how cluster analysis can be used for hypothesis generation in corpus linguistics, thereby contributing to a quantitative empirical methodology for the discipline.

The series Quantitative Linguistics publishes books on all aspects of quantitative methods and models in linguistics, text analysis and related research fields. Specifically, the scope of the series covers the whole spectrum of theoretical and empirical research, ultimately striving for an exact mathematical formulation and empirical testing of hypotheses: observation and description of linguistic data, application of methods and models, discussion of methodological and epistemological issues, modelling of language and text phenomena The rapidly growing volume of digital natural language text and the complexity of data abstracted from it have increasingly rendered traditional corpus linguistic analytical methodology obsolete. This book describes a cluster analytic methodology for generating linguistic hypotheses on the basis of data abstracted from language corpora Preface Contents List of figures 1. Introduction 2. Motivation 3. Data 4. Cluster 5. Hypothesis generation 6. Literature Review 7. Conclusion 8. Appendix References Subject index