Toward Automating a Personalized Concordancer for Data-Driven Learning: A Lexical Difficulty Filter for Language Learners
David Wible, Chin‐Hwa Kuo, Feng-yi Chien, Chengxian Wang · 2002
One of the most widely acknowledged barriers to the effectiveness of corpus and concordancing resources in the hands of language learners and educators is the lack of control over the examples retrieved. The purpose of this paper is to describe a novel tool, called the Lexical Difficulty Filter (LDF), which we have developed to increase this sort of control, specifically to filter concordance examples according to a flexible threshold of lexical difficulty. We also suggest refinements and extensions to the LDF for future research. What we present here constitutes one part of a larger effort that we are engaging in to provide precision and flexibility for language teachers and learners in their use of concordancing tools and large corpora.