1. Learner corpus analysis and error annotation Xiaofei Lu CALPER 2010 Summer Workshop July 13, 2010

2. Overview Analyzing raw corpora Error annotation  Issues in corpus annotation  Granger (2003)

3. Analyzing raw corpora Concordancing software  GOLD  AntConc Other software  CLAN

4. Issues in corpus annotation Annotation scheme and format Annotation procedure Annotation quality

5. Annotation scheme and format What are the categories you are using?  Linguistically consensual  Overspecification vs. underspecification  Use short, meaningful codes for your categories Annotation format considerations  Compatible with annotation scheme  Facilitates corpus query

6. Annotation procedure and quality Annotator training  Scheme and format  Problematic cases and disagreements Computer-assisted manual annotation  Stanford annotation tool Stanford annotation tool  UAM Corpus Tool and NoteTab UAM Corpus ToolNoteTab Inter-annotator agreement  Cohen’s Kappa Cohen’s Kappa  Online Kappa calculator Online Kappa calculator

7. Granger (2003) Learner corpora Error annotation Error statistics and analysis Integration of results into CALL Conclusion

8. Learner corpora What is a learner corpus? Difference from traditional data in SLA Difference from native language data  Frequencies  Errors From error annotation to error detection

9. Computer-aided error annotation Dagneaux, Denness and Granger (1998)  Manual correction of L2 French corpus  Elaboration of an error tagging system  Insertion of error tags and corrections  Retrieval of lists of error types and statistics  Concordance-based error analysis Tagging system  Informative but manageable  Reusable, flexible, consistent

10. Error tagging system Dulay, Burt & Krashen (1982)  System based on linguistic categories (e.g., syntax)  Surface structure alternations (e.g., omission) Granger’s (2003) three-dimensional taxonomy  Error domain  Error category  Word category

11. Error tagging system (cont.) Error domain and category  General level: grammatical, lexical, etc.  Domains subdivided into error categories  Table 1, page 468 Word category  A POS tagset with 11 major and 54 sub-categories  Makes it possible to sort errors by POS categories

12. Error tagging system (cont.) Correct forms inserted next to erroneous forms  Facilitates interpretation of error annotations  Allows for automatic sorting on correct forms Tag insertion using a menu-driven editor

13. Error statistics and analysis Error frequency by domain or (word) category  Highest ranked domains: grammar and form Error trigrams Concordancers for searching error codes  AntConc AntConc  WordSmith Tools WordSmith Tools

14. Integrating results into CALL Goal: a hypermedia CALL program  Using NLP and Communicative approaches to SLA  Traditional and NLP-enabled exercises  Automatic error diagnosis and feedback generation Error statistics and analysis used to  Select linguistic areas to focus on  Adapt exercises as a function of attested error types  Adapt NLP tools for error diagnosis

15. Integrating results into CALL (cont.) Most error-prone linguistic areas  Tense and mood, agreement  Articles, complementation, prepositions Adapting exercises  Exercises reflect type of error-prone context  Formal errors through dictation and exercises targeting specific difficulties  Attention to punctuation

16. Integrating results into CALL (cont.) Adapting NLP tools for error diagnosis  Spell checker and parser  Handles orthographic, grammatical, syntactic, and lexical errors  Not punctuation, semantic, and tense errors

17. Granger (2003) summary Effective 3-tier error annotation system  Limited number of categories per tier  Versatile automated data manipulation Limitations of error-tagging  Element of subjectivity in annotation  Focuses on misuse Usefulness of error-tagged learner corpus  Error statistics helps understand learner interlang  Helps adapt pedagogical materials and programs

18. Activity Using the Stanford annotation tool  Annotate a short text using your own scheme, or  Annotate a short learner text using Granger’s (2003) scheme Query the annotated text using AntConc