A semantic framework for textual data enrichment

In this work we present a semantic framework suitable of being used as support tool for recommender systems. Our purpose is to use the semantic information provided by a set of integrated resources to enrich texts by conducting different NLP tasks: WSD, domain classification, semantic similarities and sentiment analysis. After obtaining the textual semantic enrichment we would be able to recommend similar content or even to rate texts according to different dimensions. First of all, we describe the main characteristics of the semantic integrated resources with an exhaustive evaluation. Next, we demonstrate the usefulness of our resource in different NLP tasks and campaigns. Moreover, we present a combination of different NLP approaches that provide enough knowledge for being used as support tool for recommender systems. Finally, we illustrate a case of study with information related to movies and TV series to demonstrate that our framework works properly.

MOOCs, Open educational resources and social networking: bridging the gap between informal and formal learning

MOOCs and open educational resources (OER) provide a wealth of learning opportunities for people around the globe, many of whom have no access to formal higher education. OER are often difficult to locate and are accessed on their own without support from or dialogue with subject experts and peers. This paper looks at whether it is possible to develop effective learning communities around OER and whether these communities can emerge spontaneously and in a self-organised way without moderation. It examines the complex interplay between formal and informal learning, and examines whether MOOCs are the answer to providing effective interaction and dialogue for those wishing to study at university level for free on the Internet.