The goal of our research is to build a grammatical error-tagged corpus for Korean learners of Spoken English dubbed Postech Learner Corpus. We collected raw story-telling speech from Korean university students. Transcription and annotation using the Cambridge Learner Corpus tagset were performed by six Korean annotators fluent in English. For the annotation of the corpus, we developed an annota...

We propose a verb suggestion method which uses candidate sets and domain adaptation to incorporate error patterns produced by ESL learners. The candidate sets are constructed from a large scale learner corpus to cover various error patterns made by learners. Furthermore, the model is trained using both a native corpus and the learner corpus via a domain adaptation technique. Experiments on two ...

The paper describes the learner corpus composed of English essays written by native Russian speakers. REALEC (Russian Error-Annotated Learner English Corpus) is an error-annotated, available online corpus, now containing more than 200 thousand word tokens in almost 800 essays. It is one of the first Russian ESL corpora, dynamically developing and striving to improve both in size and in features...

We present a novel approach to grammatical error correction based on Alternating Structure Optimization. As part of our work, we introduce the NUS Corpus of Learner English (NUCLE), a fully annotated one million words corpus of learner English available for research purposes. We conduct an extensive evaluation for article and preposition errors using various feature sets. Our experiments show t...

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 this paper we present a survey of some most significant spoken English learner corpora created up to date. Spoken learner corpora which include speech generated by learners are important in many areas of research and practice, in particular, for identifying typical pronunciation errors of learners of English as a second language (ESL), English as a foreign language (EFL), or English as a lin...

With the aim of storing learner corpora as well as information about the Basque language students who wrote the texts, two different but complementary databases were created: ERREUS and IRAKAZI. Linguistic and technical information (error description, error category, tools for detection/correction...) will be stored in ERREUS, while IRAKAZI will be filled in with psycholinguistic information (e...

Learner corpora consist of texts produced by non-native speakers. In addition to these texts, some learner corpora also contain error annotations, which can reveal common errors made by language learners, and provide training material for automatic error correction. We present a novel type of error-annotated learner corpus containing sequences of revised essay drafts written by non-native speak...