Publications

Abstract

Students who are learning to write in a foreign language, often want feedback on the grammatical quality of the sentences they produce. The usual NLP approach to this problem is based on parsing student-generated text. Here, we propose a generation-based ap- proach aiming at preventing errors ("scaffolding"). In our ICALL system, the student constructs sentences by composing syntactic trees out of lexically anchored "treelets" via a graphical drag & drop user interface. A natural-language generator computes all possible grammatically well-formed sentences entailed by the student-composed tree. It provides positive feedback if the student-composed tree belongs to the well-formed set, and negative feedback otherwise. If so requested by the student, it can substantiate the positive or negative feedback based on a comparison between the student-composed tree and its own trees (informative feedback on demand). In case of negative feedback, the system refuses to build the structure attempted by the student. Frequently occurring errors are handled in terms of "malrules." The system we describe is a prototype (implemented in JAVA and C++) which can be parameterized with respect to L1 and L2, the size of the lexicon, and the level of detail of the visually presented grammatical structures.

Abstract

When you compare the behavior of two different age groups which are trying to master the same sensori-motor or cognitive skill, you are likely to discover varying learning routes: different stages, different intervals between stages, or even different orderings of stages. Such heterogeneous learning trajectories may be caused by at least six different types of factors: (1) Initial state: the kinds and levels of skills the learners have available at the onset of the learning episode. (2) Learning mechanisms: rule-based, inductive, connectionist, parameter setting, and so on. (3) Input and feedback characteristics: learning stimuli, information about success and failure. (4) Information processing mechanisms: capacity limitations, attentional biases, response preferences. (5) Energetic variables: motivation, emotional reactions. (6) Final state: the fine-structure of kinds and levels of subskills at the end of the learning episode. This applies to language acquisition as well. First and second language learners probably differ on all six factors. Nevertheless, the debate between advocates and opponents of the Fundamental Difference Hypothesis concerning L1 and L2 acquisition have looked almost exclusively at the first two factors. Those who believe that L1 learners have access to Universal Grammar whereas L2 learners rely on language processing strategies, postulate different learning mechanisms (UG parameter setting in L1, more general inductive strategies in L2 learning). Pienemann opposes this view and, based on his Processability Theory, argues that L1 and L2 learners start out from different initial states: they come to the grammar learning task with different structural hypotheses (SOV versus SVO as basic word order of German).

Abstract

Modern word processors begin to offer a range of facilities for spelling, grammar and style checking in English. For the Dutch language hardly anything is available as yet. Many commercial word processing packages do include a hyphenation routine and a lexicon-based spelling checker but the practical usefulness of these tools is limited due to certain properties of Dutch orthography, as we will explain below. In this chapter we describe a text editor which incorporates a great deal of lexical, morphological and syntactic knowledge of Dutch and monitors the orthographical quality of Dutch texts. Section 1 deals with those aspects of Dutch orthography which pose problems to human authors as well as to computational language sensitive text editing tools. In section 2 we describe the design and the implementation of the text editor we have built. Section 3 is mainly devoted to a provisional evaluation of the system.