A New Approach to Automated Text Readability Classification based on Concept Indexing with Integrated Part of Speech n gram Features
This study is about the development of a learner focused text readability indexing tool for second language learners (L2) of English. Student essays are used to calibrate the system, making it capable of providing realistic approximation of L2s' actual reading ability spectrum. The system aims to promote self directed (i.e. selfstudy) language learning and help even those L2s who can not afford formal education. In this paper, we provide a comparative review of two vectorial semantics based algorithms, namely, Latent Semantic Indexing (LSI) and Concept Indexing (CI) for text content analysis. Since these algorithms rely on the bag of words approach and inherently lack grammar related analysis, we augment them by incorporating Part of Speech (POS) n gram features to approximate syntactic complexity of the text documents. Based on the results, CI based features outperformed LSI based features in most of the experiments. Without the integration of POS n gram features, the difference between their mean exact agreement accuracies (MEAA) can reach as high as 23% in favor of CI. It has also been proven that the performance of both algorithms can be further enhanced by combining POS bi gram features, yielding as high as 95.1% and 91.9% MEAA values for CI and LSI, respectively.
RANLP