We begin by showing that the best publicly available, multiple-L1 learner corpus, the International Corpus of Learner English (Granger et al. 2009), has serious issues when used for the task of native language detection (NLD). The topic biases in the corpus are a confounding factor that result in crossvalidated performance that is misleading, for all the feature types which are traditionally us...

The availability of learner corpora, especially those which have been manually error-tagged or shallow-parsed, is still limited. This means that researchers do not have a common development and test set for natural language processing of learner English such as for grammatical error detection. Given this background, we created a novel learner corpus that was manually error-tagged and shallowpar...

In learner corpora, as in any corpus, mark-up is an important issue. One aspect of learner corpora so far largely ignored, however, is the specific question of handwriting and in particular how to mark-up handwriting anomalies, especially with learners whose native language uses a different writing system. In this paper we pose some open questions about what aspects of a learner’s handwriting m...

A learner corpus is a useful resource for developing automatic assessment techniques for implementation in a computer-assisted language learning system. However, presently, learner corpora are only helpful in terms of evaluating the accuracy of learner output (speaking and writing). Therefore, the present study proposes a learner corpus annotated with evaluation results regarding the accuracy a...

The article is devoted to spelling errors of Russian learners in a French-speaking environment. Based on 1,816 errors, the analysis focuses four mechanisms (transposition, insertion, omission, and substitution), influence contextual non-contextual (cognitive, inter- intralinguistic, extralinguistic) factors taken into account for each mechanism question. Despite multidimensional nature involved...

English as a Second Language (ESL) learners’ writings contain various grammatical errors. Previous research on automatic error correction for ESL learners’ grammatical errors deals with restricted types of learners’ errors. Some types of errors can be corrected by rules using heuristics, while others are difficult to correct without statistical models using native corpora and/or learner corpora...

Learner corpora collect the language produced by people learning their first or a second language. Natural Language Processing (NLP) deals with the representation and the automatic analysis and generation of human language. The two thus overlap in the representation and automatic analysis of learner language, which constitutes the topic of this chapter. As such, the chapter focuses on one of th...