Linguistic Knowledge and Reasoning for Error Diagnosis and Feedback Generation
DOI:
https://doi.org/10.1558/cj.v20i3.513-532Keywords:
NLP Techniques, CALL tools, Cooperative Question-Answering, SummarizationAbstract
We present four sets of NLP-based exercises for which error correction and feedback are produced by means of a rich database in which linguistic information is encoded either at the lexical or at the grammatical level. One exercise type "Question-Answering" utilizes linguistic knowledge and inferential processes on the basis of the output generated by GETARUN, a system for text understanding. GETARUN produces a complete parse of a text and a semantic mapping in line with situational semantics in the form of a Discourse Model. Another exercise, Grammcheck, uses a 'robust' version of the parser to produce suitable environments for grammatical error spotting and consequent accurate and precise feedback generation for German. The parser of GETARUN is then presented as an analytical tool for students who study Lexical Functional Grammar (LFG). Finally, exercises on "Essay Evaluation," which are cast into the more general problem of text summarization, are discussed. In this case, the system is used to perform multidocument sentence extraction on the basis of a statistically based Summarizer. This summary is then compared with the student's summary. All applications can be found at our web site, project.cgm.unive.it.
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