Describing and contextualizing events in TV news show
José Luis Redondoio Garcia, Laurens De Vocht, Raphaël Troncy, Erik Mannens, Rik Van de Walle · 2014
Describing multimedia content in general and TV programs in particular is a hard problem. Relying on subtitles to extract named entities that can be used to index fragments of a program is a common method. However, this approach is limited to what is being said in a program and written in a subtitle, therefore lacking a broader context. Furthermore, this type of index is restricted to a flat list of entities. In this paper, we combine the power of non-structured documents with structured data coming from DBpedia to generate a much richer, context aware metadata of a TV program. We demonstrate that we can harvest a rich context by expanding an initial set of named entities detected in a TV fragment. We evaluate our approach on a TV news show.