Evaluating a model to disambiguate natural language parses on the basis of user language proficiency

Abstract

This paper discusses the evaluation of an implemented user model in ICICLE, an instruction system for users writing in a second language. We show that in the task of disambiguating natural language parses, a blended model combining overlay techniques with user stereo-typing representing typical linguistic acquisition sequences successfully captures user individuality while supplementing incomplete information with stereotypic reasoning.