Creating a grammar checker for CALL by constraint relaxation: a feasibility study

Abstract

Intelligent feedback on learners’ full written sentence productions requires the use of Natural Language Processing (NLP) tools and, in particular, of a diagnosis system. Most syntactic parsers, on which grammar checkers are based, are designed to parse grammatical sentences and/or native speaker productions. They are therefore not necessarily suitable for language learners. In this paper, we concentrate on the transformation of a French syntactic parser into a grammar checker geared towards intermediate to advanced learners of French. Several techniques are envisaged to allow the parser to handle ill-formed input, including constraint relaxation. By the very nature of this technique, parsers can generate complete analyses for ungrammatical sentences. Proper labelling of where the analysis has been able to proceed thanks to a specific constraint relaxation forms the basis of the error diagnosis. Parsers with relaxed constraints tend to produce more complete, although incorrect, analyses for grammatical sentences, and several complete analyses for ungrammatical sentences. This increased number of analyses per sentence has one major drawback: it slows down the system and requires more memory. An experiment was conducted to observe the behaviour of our parser in the context of constraint relaxation. Three specific constraints, agreement in number, gender, and person, were selected and relaxed in different combinations. A learner corpus was parsed with each combination. The evolution of the number of correct diagnoses and of parsing speed, among other factors, were monitored. We then evaluated, by comparing the results, whether large scale constraint relaxation is a viable option to transform our syntactic parser into an efficient grammar checker for CALL.

On using intelligent computer-assisted language learning in real-life foreign language teaching and learning

“…On the computational side, much has been written in the last twenty years about the development of technologies that allow computers to deal with learner language (cf., e.g., Holland et al ., 1995; L'Haire & Faltin, 2003; Heift & Schulze, 2003, 2007; Dodigovic, 2005). Different approaches to parsing erroneous input have been proposed, with defenders of both constraint relaxation techniques (cf., e.g., Schwind, 1995; Vandeventer, 2001; Reuer, 2003) and malrule techniques (cf., e.g., Weischedel, Voge & James, 1978; Sleeman, 1982; Covington & Weinrich, 1991) arguing about the best way to identify errors in student sentences. The focus of these research projects typically was on the development of algorithms applied to hand-constructed examples.…”

Assessing learners’ writing using lexical frequency

“…Whilst many interesting and ingenious prototypes have been developed, mainly in languages with a high degree of regularity, such as German, their application has remained focused on the analysis of individual errors, and their use restricted to the research lab (e.g. Vandeventer, 2001). Parsing free text for meaningful feedback is still a very hard problem, and more recent developments in automatic text analysis have tended to look at more statistical approaches.…”

Sentence Reduction for Syntactic Analysis of Compound Sentences in Punjabi Language

“…These includes rule based approach [ Daniel Naber, 2003] [6], syntax based approach [Jensen et al, 1993] and statistics based approach [Attwell, 1997]. The rule based approach has been used for Dutch language [Vosse, 1992] [17], Czech and Bulgarian language [Kuboň and Plátek, 1994] [18], English language [Adriaens, 1994] [19], Swedish language [Hein, 1998] [20], German language [Schmidt-Wigger, 1998] [21], English language [Ravin, 1998] [22], Korean language [Young-Soog, 1998] [23], Danish language [Paggio, 2000] [24], French, German, and Spanish languages [Helfrich and Music, 2000] [25], French language [Vandeventer, 2001] [26], Swedish language [Carlberger et al, 2002[Carlberger et al, , 2004 [27], German language [Fliedner, 2002] [28], Swedish languge [Kann 2002 andBigert et al 2004] [13], Urdu language [Kabir et al, 2002] [30], English languge [Naber, 2003] [6], Swedish languge [Hashemi, 2003] [31], English language [Moré et al 2004], [Rider, 2005] [Park et al, 1997] [9], French ...…”