Sentence Difficulty Analysis with Local Feature Space and Global Distributional Difference

In this paper, we consider the problem of sentence difficulty analysis from various angles. Past works have endeavored to design deterministic scoring algorithms depending only on semantic and syntactic information. We propose instead not only to hire local feature space representing individual sentence with its syntactic and semantic structure, but also to consider global distributional difference among corpora. For the local feature space, we select 28 linguistic features and transform them into conjuncted and discretized form. By applying global score classification, we can show its much improved results. We test our proposed model to 1,000 sentences and get much higher accuracy than traditional learning models such as SVM and AdaBoost.