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 at 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.
Type:
Conférence
City:
Seoul
Date:
2014-04-07
Department:
Data Science
Eurecom Ref:
4246
Copyright:
© ACM, 2014. This is the author's version of the work. It is posted here by permission of ACM for your personal use. Not for redistribution. The definitive version was published in http://dx.doi.org/10.1145/2567948.2579326
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