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In recent years, the number of people studying English as a second language(ESL) has surpassed the number of native speakers. Recent work havedemonstrated the success of providing personalized content based on readingdifficulty, such as information retrieval and summarization. However, almostall prior studies of reading difficulty are designed for native speakers,rather than non-native readers. In this study, we investigate various featuresfor ESL readers, by conducting a linear regression to estimate the readinglevel of English language sources. This estimation is based not only on thecomplexity of lexical and syntactic features, but also several novel concepts,including the age of word and grammar acquisition from several sources, wordsense from WordNet, and the implicit relation between sentences. By employingBayesian Information Criterion (BIC) to select the optimal model, we find thatthe combination of the number of words, the age of word acquisition and theheight of the parsing tree generate better results than alternative competingmodels. Thus, our results show that proposed second language reading difficultyestimation outperforms other first language reading difficulty estimations.

Characterizing the Influence of Features on Reading Difficulty Estimation for Non-native Readers