Word Difficulty Prediction Using Convolutional Neural Networks

Most text-simplification systems require an indicator of the complexity of the words. The prevalent approaches to word difficulty prediction are based on manual feature engineering. Using deep learning based models are largely left unexplored due to their comparatively poor performance. In this paper we explore the use of one of such in predicting the difficulty of words. We treat the problem as a binary classification problem. We train traditional machine learning models and evaluate their performance on the task. Removing dependency on frequency of previously acquired words for measuring difficulty was one of our primary aims. Then we analyze a convolutional neural network based prediction model which operates at the character level and evaluate its efficiency compared to others.