This paper describes an ongoing project in which we are collecting a learner corpus of Arabic, developing a tagset for error annotation and performing Computer-aided Error Analysis (CEA) on the data. We adapted the French Interlanguage Database FRIDA tagset (Granger, 2003a) to the data. We chose FRIDA in order to follow a known standard and to see whether the changes needed to move from a Frenc...

This paper describes a pilot study in which we collected a small learner corpus of Arabic, developed a tagset for errorannotation of Arabic learner data, tagged the data for error, and performed simple Computer-aided Error Analysis (CEA). Language Learner Corpora and Applications Learner corpora research uses the methods and tools of Second Language Acquisition (SLA) studies and corpus linguist...

We present a novel method to automatically improve the accurrcy of part-of-speech taggers on learner language. The key idea underlying our approach is to exploit the structure of a typical language learner task and automatically induce POS information for out-of-vocabulary (OOV) words. To evaluate the effectiveness of our approach, we add manual POS and normalization information to an existing ...

We present a novel approach for automatic collocation error correction in learner English which is based on paraphrases extracted from parallel corpora. Our key assumption is that collocation errors are often caused by semantic similarity in the first language (L1language) of the writer. An analysis of a large corpus of annotated learner English confirms this assumption. We evaluate our approac...

This talk is concerned with using syntactic annotation of learner language and the corresponding target hypothesis to find structural acquisition difficulties in German as a foreign language. Using learner data for the study of acquisition patterns is based on the idea that learners do not produce random output but rather possess a consistent internal grammar (interlanguage; cf. [1] and many ot...

A learner’s language data of speaking, writing, listening, and reading have been compiled for a learner corpus in this study. The language data consist of linguistic output and language processing. Linguistic output refers to data of pronunciation, sentences, listening comprehension rate, and reading comprehension rate. Language processing refers to processing time and learners’ self-judgment o...

This chapter centers on the nuisance caused by passive voice and attributive clauses in student translations. With the use of learner corpus, calculation, categorization, and annotation functions enable analysis of common linguistic features in student translators. The aim of this study is to correct learners’ under-use, over-use, and misuse of terms and linguistic structures. By incorporating ...

We investigate the use of web search queries for detecting errors in non-native writing. Distinguishing a correct sequence of words from a sequence with a learner error is a baseline task that any error detection and correction system needs to address. Using a large corpus of error-annotated learner data, we investigate whether web search result counts can be used to distinguish correct from in...

We show how partial models of natural language syntax (manually written DCGs, with parameters estimated from a parsed corpus) can be automatically extended when trained upon raw text (using MDL). We also show how we can use a parsed corpus as an alternative constraint upon estimation. Empirical evaluation suggests that a parsed corpus is more informative than a MDL-based prior. However , best r...