Current lexical simplification approaches rely heavily on heuristics and corpus level features that do not always align with human judgment. We create a human-rated word-complexity lexicon of 15,000 English words and propose a novel neural readability ranking model with a Gaussian-based feature vectorization layer that utilizes these human ratings to measure the complexity of any given word or phrase. Our model performs better than the state-of-the-art systems for different lexical simplification tasks and evaluation datasets. Additionally, we also produce SimplePPDB++, a lexical resource of over 10 million simplifying paraphrase rules, by applying our model to the Paraphrase Database (PPDB).
@article{arxiv.1810.05754,
title = {A Word-Complexity Lexicon and A Neural Readability Ranking Model for Lexical Simplification},
author = {Mounica Maddela and Wei Xu},
journal= {arXiv preprint arXiv:1810.05754},
year = {2018}
}