Learning to Predict Readability using Diverse Linguistic Features

In this paper we consider the problem of building a system to predict readability of natural-language documents. Our system is trained using diverse features based on syntax and language models which are generally indicative of readability. The experimental results on a dataset of documents from a mix of genres show that the predictions of the learned system are more accurate than the predictions of naive human judges when compared against the predictions of linguistically-trained expert human judges. The experiments also compare the performances of different learning algorithms and different types of feature sets when used for predicting readability.

• Type: Journal
• Year: 2010
• Where: COLING
• Authors: Rohit J. Kate, Xiaoqiang Luo, Siddharth Patwardhan, Martin Franz, Radu Florian, Raymond J. Mooney, Salim Roukos, Chris Welty
• Keywords: COLING 2010, Computational Linguistics, Human Judges, Naive Human Judges, Readability