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Record Nr. |
UNINA9910829068003321 |
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Titolo |
Corpus methods for semantics : quantitative studies in polysemy and synonymy / / edited by Dylan Glynn, Justyna A. Robinson ; contributors, Timothy Colleman [and twenty-two others] |
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Pubbl/distr/stampa |
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Amsterdam, Netherlands ; ; Philadelphia, Pennsylvania : , : John Benjamins Publishing Company, , 2014 |
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©2014 |
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ISBN |
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Descrizione fisica |
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1 online resource (553 p.) |
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Collana |
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Human Cognitive Processing, , 1387-6724 ; ; Volume 43 |
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Disciplina |
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Soggetti |
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Semantics |
Cognitive grammar |
Computational linguistics |
Polysemy |
Corpora (Linguistics) |
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Lingua di pubblicazione |
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Formato |
Materiale a stampa |
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Livello bibliografico |
Monografia |
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Note generali |
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Description based upon print version of record. |
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Nota di bibliografia |
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Includes bibliographical references at the end of each chapters and indexes. |
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Nota di contenuto |
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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-verbs3. 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- |
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specific constructions; 5. Conclusion; References; Quantifying polysemy in cognitive sociolinguistics; 1. Polysemy; 2. Scope of the study; 3. Data and method |
4. Hierarchical agglomerative clustering5. 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 |
ReferencesA 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 semantics1. 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 |
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Sommario/riassunto |
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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 Linguis |
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