Adapting Web content for low-literacy readers by using lexical elaboration and named entities labeling

This paper presents an approach for assisting low-literacy readers in accessing Web online information. The oEducational FACILITAo tool is a Web content adaptation tool that provides innovative features and follows more intuitive interaction models regarding accessibility concerns. Especially, we propose an interaction model and a Web application that explore the natural language processing tasks of lexical elaboration and named entity labeling for improving Web accessibility. We report on the results obtained from a pilot study on usability analysis carried out with low-literacy users. The preliminary results show that oEducational FACILITAo improves the comprehension of text elements, although the assistance mechanisms might also confuse users when word sense ambiguity is introduced, by gathering, for a complex word, a list of synonyms with multiple meanings. This fact evokes a future solution in which the correct sense for a complex word in a sentence is identified, solving this pervasive characteristic of natural languages. The pilot study also identified that experienced computer users find the tool to be more useful than novice computer users do.

An enhanced content selection mechanism for personalization of video news programmes

In this paper, we propose a content selection framework that improves the users` experience when they are enriching or authoring pieces of news. This framework combines a variety of techniques to retrieve semantically related videos, based on a set of criteria which are specified automatically depending on the media`s constraints. The combination of different content selection mechanisms can improve the quality of the retrieved scenes, because each technique`s limitations are minimized by other techniques` strengths. We present an evaluation based on a number of experiments, which show that the retrieved results are better when all criteria are used at time.

Taking advantage of contextualized interactions while users watch TV

While watching TV, viewers use the remote control to turn the TV set on and off, change channel and volume, to adjust the image and audio settings, etc. Worldwide, research institutes collect information about audience measurement, which can also be used to provide personalization and recommendation services, among others. The interactive digital TV offers viewers the opportunity to interact with interactive applications associated with the broadcast program. Interactive TV infrastructure supports the capture of the user-TV interaction at fine-grained levels. In this paper we propose the capture of all the user interaction with a TV remote control-including short term and instant interactions: we argue that the corresponding captured information can be used to create content pervasively and automatically, and that this content can be used by a wide variety of services, such as audience measurement, personalization and recommendation services. The capture of fine grained data about instant and interval-based interactions also allows the underlying infrastructure to offer services at the same scale, such as annotation services and adaptative applications. We present the main modules of an infrastructure for TV-based services, along with a detailed example of a document used to record the user-remote control interaction. Our approach is evaluated by means of a proof-of-concept prototype which uses the Brazilian Digital TV System, the Ginga-NCL middleware.