Predicting and compensating for lexicon access errors

Abstract

Learning a foreign language is a long, error-prone process, and much of a learner's time is effectively spent studying vocabulary. Many errors occur because words are only partly known, and this makes their mental storage and retrieval problematic. This paper describes how an intelligent interface may take advantage of the access structure of the mental lexicon to help predict the types of mistakes that learners make, and thus compensate for them. We give two examples, firstly a dictionary interface which uses search-by-similarity to circumvent the tip-of-the-tongue problem, and secondly an adaptive test generator which leverages user errors to generate plausible multiple-choice distractors...