This paper presents an overview of the research conducted within the FreeText project to build an automatic error diagnosis system for learners of French as a foreign language. After a brief review of the main features of the project and of the learner corpus collected and used within the project, the paper focuses on the error diagnosis system itself and, more specifically, on two of its components: (a) a syntactic checker making use of two different diagnosis techniques to detect errors of purely grammatical nature and (b) a sentence comparison tool which compares learners' answers with those stored in the system to detect possible semantic discrepancies such as referents or word usage. Advantages of such an automatic system and ideas for further research are then discussed.
This paper illustrates the usefulness of natural language processing (NLP) tools for computer assisted language learning (CALL) through the presentation of three NLP tools integrated within a CALL software for French. These tools are (i) a sentence structure viewer; (ii) an error diagnosis system; and (iii) a conjugation tool. The sentence structure viewer helps language learners grasp the structure of a sentence, by providing lexical and grammatical information. This information is derived from a deep syntactic analysis. Two different outputs are presented. The error diagnosis system is composed of a spell checker, a grammar checker, and a coherence checker. The spell checker makes use of alpha-codes, phonological reinterpretation, and some ad hoc rules to provide correction proposals. The grammar checker employs constraint relaxation and phonological reinterpretation as diagnosis techniques. The coherence checker compares the underlying "semantic" structures of a stored answer and of the learners' input to detect semantic discrepancies. The conjugation tool is a resource with enhanced capabilities when put on an electronic format, enabling searches from inflected and ambiguous verb forms.
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