ARIDA: An Arabic Interlanguage Database and Its Applications: A Pilot Study

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 linguistics to gain better insights into authentic learner language at different levels – lexis, grammar, and discourse. One application of learner corpora is Contrastive Interlanguage Analysis (CIA), which involves two types of comparison – 1) native speech (NS) vs. non-native speech (NNS) to highlight the features of nativeness and non-nativeness of learner language; 2) two or more varieties of NNS to determine whether non-native features are limited to one group of non-native speakers (in which case it is most probably a transfer-related phenomenon), or whether they are shared by several groups of learners with different mother tongue backgrounds (which would point to a developmental issue). Another application is Computer-aided Error Analysis (CEA) for identifying the sources of error (L1 interference, features of novice writing in the new culture, limited vocabulary and language structure, etc.). For this application, error annotation is essential.