Corpus-Based Tools for Computer-Assisted Acquisition of Reading Abilities in Cognate Languages

This paper presents an approach to computer-assisted teaching of reading abilities using corpus data. The approach is supported by a set of tools for automatically selecting and classifying texts retrieved from the Internet. The approach is based on a linguistic model of textual cohesion which describes relations between larger textual units that go beyond the sentence level. We show that textual connectors that link such textual units reliably predict different types of texts, such as 'information' and 'opinion': using only textual connectors as features, an SVM classifier achieves an F-score of between 0.85 and 0.93 for predicting these classes. The tools are used in our project on teaching reading skills in a cognate foreign language (L3) which is cognate to a known foreign language (L2).