Automatic Treatment and Analysis of Learner Corpus Data

Name: Automatic Treatment and Analysis of Learner Corpus Data
Brand: John Benjamins Publishing Company
Price: 118.99 EUR
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Nicolas Ballier Ana Díaz-Negrillo Paul Thompson(Herausgeber*in)

John Benjamins Publishing Company

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Erschienen am 18. Dezember 2013

VI, 314 Seiten

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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
Developing corpus interoperability for phonetic investigation of learner corpora
1. 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
4.1 Tokenisation and categorisation of realisations and learner phonetic errors
4.2 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
2.2 Corpora in language acquisition research
2.3 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
4.1 Data collection
4.2 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
3.3 Manual error tagging
3.4 Parsing learner data
4. Summary
References
Section 3. Automatic approaches to the identification of learner language features in learner corpus data
Using learner corpora for automatic error detection and correction
1. Introduction
2. System development guided by annotated learner corpora
2.1 Cambridge Learner Corpus
2.2 Distribution of errors in CLC
2.3 Morphological confusions and errors
3. Training data for machine-learned systems
3.1 Statistical methods for learner error detection
3.2 Using well-formed data for training
3.3 Using annotated learner data for training
4. Evaluation of ESL error detection systems
4.1 Evaluation metrics
4.2 Annotation issues
4.3 Future directions for evaluation and annotation
5. Summary
References
Automatic suprasegmental parameter extraction in learner corpora
1. Introduction
1.1 Corpus
1.2 Automatic parameter extraction
2. Benchmarking automatic against manual segmentation
2.1 Duration of read passages
2.2 Speech rate
2.3 Vowel duration
3. Alternative suprasegmental parameters and automatic classification
3.1 Voiced (VO) vs. voiceless (UV) intervals
3.2 Pitch extraction
3.3 Intensity
4. Automatic classification
5. Discussion and conclusion
References
Criterial feature extraction using parallel learner corpora and machine learning
1. Introduction
2. Method
2.1 The corpora
2.2 Problems of manual error-tagging
2.3 Parallel corpora and edit distance
2.4 Extractions of errors and data analysis
3. Results 1: Correspondence Analysis and Variability-based Neighbour Clustering
3.1 Distributions of surface taxonomy errors
3.2 Correspondence analysis over the different error types across school years
3.3 Refining the analysis by using Variability-based Neighbour Clustering
4. Results 2: Random forest
4.1 Extraction of addition and omission errors
4.2 Results of random forest
5. Discussion
References
Section 4. Analysis of learner corpus data
Phonological acquisition in the French-English interlanguage
1. Background
2. Subsets and predictions
3. Method and results
3.1 Subset 1 (S1)
3.2 Subset 2 (S2)
3.3 Subset S3 (S3)
4. Conclusion
References
Prosody in a contrastive learner corpus
1. Introduction
2. ANGLISH: A learner corpus
2.1 Constitution and description: Objectives
2.2 Conception and design
3. Experiment
3.1 Data
3.2 Method
3.3 Results
4. Discussion
5. Conclusion
References
A corpus-based comparison of syntactic complexity in NNS and NS university students' writing
1. Introduction
2. Syntactic complexity in second language writing
3. Measuring L2 syntactic complexity
4. Method
4.1 Data
4.2 Research questions
4.3 Analysis
5. Results and discussion
5.1 Research question 1
5.2 Research question 2
6. Conclusions and implications
References
Analysing coherence in upper-intermediate learner writing
1. Introduction
2. Conceptualizations of coherence in learner writing
3. Coherence and cohesion - A close match?
3.1 Rhetorical Structure Theory (RST) as a model for the analysis of coherence
3.2 RST and learner writing
4. The study - Using RST to analyse coherence in learner writing
4.1 Data
4.2 Method
4.3 Initial results
5. Potential and limitations of using RST for the analysis of coherence in learner writing
6. Conclusion and outlook
References
Statistical tests for the analysis of learner corpus data
1. Introduction
1.1 General introduction
1.2 A very brief view on caveats regarding learner corpus research
1.3 The corpus data: The genitive alternation (of- vs. s-genitives)
2. Elementary statistical tests
2.1 Two-dimensional frequency tables: Chi-squared tests
2.2 Measures of central tendency
3. A primer on multifactorial methods: Logistic regression
3.1 Logistic regressions with two categorical independent variables
3.2 Logistic regressions with one categorical and one numeric independent variable
4. Concluding remarks
4.1 Conditional inference trees: An alternative to regressions
4.2 Pointers to additional references
References
Index

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