(Generalized Linear) Mixed?Effects Modeling: A Learner Corpus Example

Building a learner corpus

The paper describes a corpus of texts produced by non-native speakers of Czech. We discuss its annotation scheme, consisting of three interlinked levels to cope with a wide range of error types present in the input. Each level corrects different types of errors; links between the levels allow capturing errors in word order and complex discontinuous expressions. Errors are not only corrected, bu...

Modeling the Example Life-Cycle in an Online Classification Learner

An online classification system maintained by a learner can be subject to latency and filtering of training examples which can impact on its classification accuracy especially under concept drift. A life-cycle model is developed to provide a framework for studying this problem. Meta data emerges from this model which it is proposed can enhance online learning systems. In particular, the definit...

Generalized Linear Modeling

Building a Large Annotated Corpus of Learner English: The NUS Corpus of Learner English

We describe the NUS Corpus of Learner English (NUCLE), a large, fully annotated corpus of learner English that is freely available for research purposes. The goal of the corpus is to provide a large data resource for the development and evaluation of grammatical error correction systems. Although NUCLE has been available for almost two years, there has been no reference paper that describes the...

The COPLE2 corpus: a learner corpus for Portuguese

We present the COPLE2 corpus, a learner corpus of Portuguese that includes written and spoken texts produced by learners of Portuguese as a second or foreign language. The corpus includes at the moment a total of 182,474 tokens and 978 texts, classified according to the CEFR scales. The original handwritten productions are transcribed in TEI compliant XML format and keep record of all the origi...