#entity linking

This paper describes our (winning!) submission to the Developers Challenge at WoLE2013, “Doing Good by Linking Entities”. We present a fully automatic system – called “Semantic TED” – which provides intelligent suggestions in the form of links to Wikipedia articles for video streams in multiple languages, based on the subtitles that accompany the visual content. The system is applied to online conference talks. In particular, we adapt a recently proposed semantic linking approach for streams of television broadcasts to facilitate generating contextual links while a TED talk is being viewed. TED is a highly popular global conference series covering many research domains; the publicly available talks have accumulated a total view count of over one billion at the time of writing. We exploit the multi-linguality of Wikipedia and the TED subtitles to provide contextual suggestions in the language of the user watching a video. In this way, a vast source of educational and intellectual content is disclosed to a broad audience that might otherwise experience difficulties interpreting it.

D. Odijk, E. Meij, D. Graus, and T. Kenter. Multilingual semantic linking for video streams: making “ideas worth sharing” more accessible. In Proceedings of the 2nd International Workshop on Web of Linked Entities (WoLE 2013), 2013. BibTex:

Our OAIR'13 paper got accepted.

Television is changing. Increasingly, broadcasts are consumed interactively. This allows broadcasters to provide consumers with additional background information that they may bookmark for later consumption. To support this type of functionality, we consider the task of linking a textual streams derived from live broadcasts to Wikipedia. While link generation has received considerable attention in recent years, our task has unique demands that require an approach that needs to (i) be high-precision oriented, (ii) perform in real-time, (iii) work in a streaming setting, and (iv) typically, with a very limited context. We propose a learning to rerank approach that significantly improves over a strong baseline in terms of effectiveness and whose processing time is very short. We extend this approach, leveraging the streaming nature of the textual sources that we link by modeling context as a graph. We show how our graph-based context model further improves effectiveness. For evaluation purposes we create a dataset of segments of television subtitles that we make available to the research community.