# Corpus Methods for Semantics : Quantitative studies in polysemy and synonymy.

Series: Human Cognitive ProcessingPublisher: Amsterdam : John Benjamins Publishing Company, 2014Copyright date: ©2014Description: 1 online resource (553 pages)Content type:- text

- computer

- online resource

- 9789027270337

- 401/.43

- P325 -- .C595 2014eb

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Corpus Methods for Semantics -- Editorial page -- Title page -- LCC data -- Table of contents -- Contributors -- Outline -- 1. Aim of the volume -- 2. Structure and summary -- References -- Section 1. Polysemy and synonymy -- Polysemy and synonymy: Cognitive theory and corpus method -- 1. Introduction: Theory and method -- 2. Polysemy and synonymy: Definition, object and operationalisation -- 3. Complexity and sampling: The need for quantification -- 4. Modelling meaning. Multidimensional patterns and prototype effects -- References -- Competing 'transfer' constructions in Dutch: The case of ont-verbs -- 1. Introduction -- 2. Introducing the Dutch ont-verbs -- 3. Methodology of the case study -- 4. The results of the present-day investigation -- 5. A diachronic perspective -- 6. Conclusion -- References -- Appendix -- Rethinking constructional polysemy: The case of the English conative construction -- 1. Introduction -- 2. The conative construction -- 3. A collexeme analysis of the conative construction -- 4. A collexeme analysis of verb-class-specific constructions -- 5. Conclusion -- References -- Quantifying polysemy in cognitive sociolinguistics -- 1. Polysemy -- 2. Scope of the study -- 3. Data and method -- 4. Hierarchical agglomerative clustering -- 5. Hierarchical agglomerative cluster analysis of collected data -- 6. Logistic regression -- 7. Decision tree analysis -- 8. Summary and discussion of results -- References -- The many uses of run: Corpus methods and Socio-Cognitive Semantics -- 1. Introduction -- 2. Usage-based Cognitive Semantics -- 3. Case study: run in America and Britain in diaries and conversation -- 4. Summary -- References -- Visualizing distances in a set of near-synonyms: Rather, quite, fairly, and pretty -- 1. Introduction -- 2. Previous research -- 3. Method -- 4. Results -- 5. Discussion and conclusion.

References -- A case for the multifactorial assessment of learner language: The uses of may and can in French-English interlanguage -- 1. Introduction and overview -- 2. Setting the stage -- 3. Data and methods -- 4. Results and discussion -- 5. Concluding remarks -- References -- Dutch causative constructions: Quantification of meaning and meaning of quantification -- 1. Introduction -- 2. Dutch causative constructions -- 3. Data and variables -- 4. Statistical analysis -- 5. Linguistic interpretation of the statistical models -- 6. Conclusion -- References -- The semasiological structure of Polish myśleć 'to think': A study in verb-prefix semantics -- 1. Introduction -- 2. Introspective conceptual analysis of the prefixed forms of myśleć 'to think' in Polish -- 3. The corpus -- 4. Feature annotation -- 5. Multivariate analysis of the results of feature annotation -- 6. Conclusion -- References -- A multifactorial corpus analysis of grammatical synonymy: The Estonian adessive and adposition peal -- 1. Introduction -- 2. The Estonian adessive case and the adposition peal 'on' -- 3. The data sample -- 4. Corpus-linguistic operationalizations and monofactorial results -- 5. Multifactorial results. Logistic regression analysis -- 6. Conclusion -- References -- A diachronic corpus-based multivariate analysis of "I think that" vs. "I think zero" -- 1. Introduction -- 2. Review of the literature -- 3. Data and methods of the current study -- 4. Discussion of the results -- 5. Discussion -- 6. Conclusion -- References -- Section 2. Statistical techniques -- Techniques and tools: Corpus methods and statistics for semantics -- 1. Introduction -- 2. Collocations and features: Two approaches to corpora -- 3. Statistical techniques and tools -- References -- Statistics in R: First steps -- 1. Installing R -- 2. Commands -- 3. The data file.

4. Importing the data into R -- 5. Making changes to a dataframe in R -- 6. Converting data formats -- 7. Making charts -- 8. Working with scripts -- 9. Extending functionality with packages -- 10. Going further -- References -- Appendix: The tablebind-script -- Frequency tables: Tests, effect sizes, and explorations -- 1. Introduction -- 2. How to analyze frequency tables -- 3. Conclusion -- References -- Collostructional analysis: Measuring associations between constructions and lexical elements -- 1. Introduction -- 2. Collexeme analysis -- 3. Distinctive collexeme analysis -- 4. Covarying-collexeme analysis -- 5. Concluding remarks -- References -- Cluster analysis: Finding structure in linguistic data -- 1. Introduction -- 2. Steps in conducting a cluster analysis -- 3. By way of conclusion -- References -- Appendix -- Correspondence analysis: Exploring data and identifying patterns -- 1. A technique for visualising correlations in categorical data -- 2. Performing and interpreting correspondence analysis in R -- 3. Choice - correspondence or cluster -- 4. Further reading -- References -- Logistic regression: A confirmatory technique for comparisons in corpus linguistics -- 1. Introduction -- 2. Simple logistic regression analysis -- 3. Multiple logistic regression analysis -- 4. Example R code -- 5. Model diagnostics -- 6. Variable selection -- 7. Which conditions should my data set meet? -- 8. Beyond the limits of traditional binomial logistic regression -- 9. Further reading -- References -- Name index -- Subject index -- Dutch causative constructions: Quantification of meaning and meaning of quantification -- 1. Introduction -- 2. Dutch causative constructions -- 3. Data and variables -- 4. Statistical analysis -- 5. Linguistic interpretation of the statistical models -- 6. Conclusion -- References.

This text offers an introduction to binary logistic regression, a confirmatory technique for statistically modelling the effect of one or several predictors on a binary response variable. It is explained why logistic regression is exceptionally well suited for the comparison of near-synonyms in corpus data; the technique allows the researcher to identify the different factors that have an impact on the choice between near synonyms, and to tease apart their respective effects. Moreover, the technique is well suited to deal with the type of unbalanced data sets that are typical of Corpus Linguistics. First, we describe in which contexts logistic regression is applicable and we give examples of the types of research questions for which it is an appropriate tool. Next, we explain why and how logistic regression analysis is different from linear regression analysis and we illustrate how the output of logistic regression analysis can be interpreted, using the study of an alternation pattern in Dutch as our example. The R code used in the case study is explained in detail and an URL is given from which R code and data sets can be downloaded. Finally, suggestions for further reading are given.

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Electronic reproduction. Ann Arbor, Michigan : ProQuest Ebook Central, 2019. Available via World Wide Web. Access may be limited to ProQuest Ebook Central affiliated libraries.

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