WWW 2014, 2nd International Workshop on Social News on the Web (SNOW 2014), April 7, 2014, Seoul, South Korea
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 WWW 2014, 2nd International Workshop on Social News on the Web (SNOW 2014), April 7, 2014, Seoul, South Korea http://dx.doi.org/10.1145/2567948.2579326
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