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Cluster analysis for corpus linguistics / by Hermann Moisl.

De Gruyter DG Plus DeG Package 2015 Part 1 Available online

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EBSCOhost Academic eBook Collection (North America) Available online

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Ebook Central Academic Complete Available online

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Format:
Book
Author/Creator:
Moisl, Hermann, 1949- author.
Series:
Quantitative linguistics ; 66.
Quantitative linguistics ; 66
Language:
English
Subjects (All):
Corpora (Linguistics)--Data processing.
Corpora (Linguistics).
Cluster analysis--Data processing.
Cluster analysis.
Natural language processing (Computer science).
Quantitative linguistics.
Computational linguistics.
Physical Description:
1 online resource (398 p.)
Edition:
1st ed.
Place of Publication:
Berlin ; Boston : De Gruyter, [2015]
Language Note:
English
Summary:
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.
Contents:
Front matter
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
Notes:
Description based upon print version of record.
Includes bibliographical references (pages 311-358) and index.
Description based on print version record.
ISBN:
9783110363814
311036381X
9783110393170
3110393174
OCLC:
1013948543

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