Automatic treatment and analysis of learner corpus data /
edited by Ana Díaz-Negrillo, Nicolas Ballier, Paul Thompson.
Bok Engelsk 2013 · Electronic books.
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| Omfang | 1 online resource (320 p.)
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| Opplysninger | Description based upon print version of record.. - Automatic Treatment and Analysis of Learner Corpus Data; Editorial page; Title page; LCC data; Table of contents; Section 1. Introduction; Introduction; References; Learner corpora; 1. Introduction; 2. Corpora types, processing and annotation; 2.1 Types of learner corpora; 2.2 Annotation; 3. Uses and users of learner corpus data; 3.1 Overview; 3.2 Foreign language teaching; 3.3 Second language acquisition research; 3.4 Corpus and computational linguistics; 4. Looking forwards; References; Section 2. Compilation, annotation and exchangeability of learner corpus data. - 2.2 Corpora in language acquisition research2.3 An overview of learner corpora and learner corpus research; 2.4 L2 Spanish learner corpora: Introducing CEDEL2; 3. Design principles in learner corpora for SLA purposes: CEDEL2, a case study; 3.1 Principle 1. Content selection; 3.2 Principle 2. Representativeness; 3.3 Principle 3. Contrast; 3.4 Principle 4. Structural criteria; 3.5 Principle 5. Annotation; 3.6 Principle 6. Sample size; 3.7 Principle 7. Documentation; 3.8 Principle 8. Balance; 3.9 Principle 9. Topic; 3.10 Principle 10. Homogeneity; 3.11 Conclusion; 4. Current status of CEDEL2. - 3.3 Manual error tagging. - 4.1 Data collection4.2 Data distribution; 4.3 Source of data; 4.4 Preliminary segmentation and annotation; 4.5 CEDEL2: Next steps; 5. Learner corpora: The way forward; 6. Conclusion; References; Appendices; Competing target hypotheses in the Falko corpus; 1. Introduction: Why corpus architecture matters; 2. What kind of information should a learner corpus provide and what kind of data is needed?; 2.1 POS & lemmas; 2.2 Target hypotheses; Error exponent; Conflicting spans; 2.3 Stand-off models; 3. Case study: Falko; 3.1 Target hypotheses in the Falko essay corpus; 3.2 Automatic error tagging. - 4.1 Tokenisation and categorisation of realisations and learner phonetic errors4.2 Modelling; 4.3 Comparing with native data (corpus interoperability); 5. From spoken learner corpora to spoken learner databases; 5.1 Textual datasets; 5.2 XML and XML tools; 5.3 Working with customized interface of Praat; 5.4 An alternative stance: WinPitch; 5.5 An incoming mixed model?; 6. The advent of spoken databases vs. speech databases; References; Learner corpora and second language acquisition; 1. Introduction; 2. Learner corpora in SLA research; 2.1 A bias in second language research. - Developing corpus interoperability for phonetic investigation of learner corpora1. Introduction; 2. Processing and annotating spoken data; 2.1 A tentative typology of spoken learner corpora; 2.2 Existing annotation layers in phonetic corpora, corpus comparability and interoperability; 2.3 Comparing with native corpora; 3. Some of the limits of automatisation; 3.1 The limits of phonetic annotation (forced alignments); 3.2 Some syllabification issues; 3.3 Prosodic annotation; 3.4 Speaker-dependent models?; 3.5 The uses of automation (caveats); 4. Challenges and recommendations. - This paper is an overview of several basic statistical tools in corpus-based SLA research. I first discuss a few issues relevant to the analysis of learner corpus data. Then, I illustrate a few widespread quantitative techniques and statistical visualizations and exemplify them on the basis of corpus data on the genitive alternation - the of-genitive vs. the s-genitive from German learners and native speakers of English. The statistical methods discussed include a test for differences between frequencies (the chi-squared test), tests for differences between means/medians (the
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| ISBN | 90-272-7095-3
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