Domain Adapted Distant Supervision for Pedagogically Motivated Relation Extraction

In this paper we present a relation extraction system that given a text extracts pedagogically motivated relation types, as a previousstep to obtaining a semantic representation of the text which will make possible to automatically generate questions for reading comprehension. The system maps pedagogically motivated relations with relations from ConceptNet and deploys Distant Supervisionfor relation extraction. We run a study on a subset of those relationships in order to analyse the viability of our approach. For that, webuild a domain-specific relation extraction system and explore two relation extraction models: a state-of-the-art model based on transferlearning and a discrete feature based machine learning model. Experiments show that the neural model obtains better results in termsof F-score and we yield promising results on the subset of relations suitable for pedagogical purposes. We thus consider that distantsupervision for relation extraction is a valid approach in our target domain, i.e. biology.