  Complex Word Identification (CWI) aims to detect words within a text that a
reader may find difficult to understand. It has been shown that CWI systems can
improve text simplification, readability prediction and vocabulary acquisition
modelling. However, the difficulty of a word is a highly idiosyncratic notion
that depends on a reader's first language, proficiency and reading experience.
In this paper, we show that personal models are best when predicting word
complexity for individual readers. We use a novel active learning framework
that allows models to be tailored to individuals and release a dataset of
complexity annotations and models as a benchmark for further research.
