This article presents a set of interactive parser-based CALL programs for North Sámi. The programs are based on a finite state morphological analyser and a constraint grammar parser which is used for syntactic analysis and navigating in the dialogues. The analysers provide effective and reliable handling of a wide variety of user input. In addition, relaxation of the grammatical analysis of the...

The purpose of this study was to evaluate whether increased utterance length and grammatical complexity are associated with changes in frequency of systematic speech errors (i.e., phonological processes, or sound changes affecting an entire class of sounds or sound sequence [Edwards & Shriberg, 1983]), and nonsystematic speech errors (i.e., a word or string of words that apparently deviates fro...

Deviation of real speech from grammatical ideals due to disfluency and other speech errors presents potentially serious problems for the language learner. While infants may initially benefit from attending primarily or solely to infant-directed speech, which contains few grammatical errors, older infants may listen more to adult-directed speech. In a first experiment, Post-verbal infants prefer...

In this paper, we propose a new framework for online handwritten mathematical expression recognition. In this approach, we consider handwritten mathematical expressions as the output of stroke generation processes based on a stochastic context-free grammar which generates handwritten expressions stochastically. We estimate the most likely expression candidate derived from the grammar, rather th...

This paper presents research on building a model of grammatical error correction, for preposition errors in particular, in English text produced by language learners. Unlike most previous work which trains a statistical classifier exclusively on well-formed text written by native speakers, we train a classifier on a large-scale, error-tagged corpus of English essays, relying on contextual and g...

This paper presents research on building a model of grammatical error correction, for preposition errors in particular, in English text produced by language learners. Unlike most previous work which trains a statistical classifier exclusively on well-formed text written by native speakers, we train a classifier on a large-scale, error-tagged corpus of English essays written by EFL learners, rel...