2022/05/05 by Sian Gooding, Manuel Tragut, Gooding, Sian +1 · 1 citation
Computer Science · #Text Readability and Simplification #Natural Language Processing Techniques #Topic Modeling
paper · pdf · doi:10.48550/arxiv.2205.02564
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.