Automatic Recognition of Text Difficulty from Consumers Health Information

Internet is used as one of major sources of health information. However, some studies show that the readability of health information presented on health Web sites is difficult for many consumers. Readability formulas usually measure difficulty of writing style, instead of difficulty of content. In order to recommend health information with appropriate reading level to consumers, we investigate the feasibility of identifying text difficulty of health information using machine learning methods. Support vector machine is used to classify consumer health information into easy to read and reading level for the general public. Three feature sets: surface linguistic features, word difficulty features, unigrams and their combinations are compared in terms of classification accuracy. Unigram features alone reach an accuracy of 80.71%, and the combination of three feature sets is the most effective in classification with accuracy of 84.06%. They are significantly better than surface linguistic features, word difficulty features and their combination