Truly effective and practical educational systems will only be achievable when they have the ability to fully recognize deep relationships between a learner’s interpretation of a subject and the desired conceptual understanding. In this paper, we take important steps in this direction by introducing a new representation of sentences – Minimal Meaningful Propositions (MMPs), which will allow us to significantly improve the mapping between a learner’s answer and the ideal response. Using this technique, we make significant progress towards highly scalable and domain independent educational systems, that will be able to operate without human intervention. Even though this is a new task, we show very good results both for the extraction of MMPs and for classification with respect to their importance.

Given the large amounts of online textual documents available these days, e.g., news articles, weblogs, and scientific papers, effective methods for extracting keyphrases, which provide a high-level topic description of a document, are greatly needed.In this paper, we propose a supervised model for keyphrase extraction from research papers, which are embedded in citation networks.To this end, we design novel features based on citation network information and use them in conjunction with traditional features for keyphrase extraction to obtain remarkable improvements in performance over strong baselines.