Investigating domain-independent NLP techniques for precise target selection in video hyperlinking

Abstract : Automatic generation of hyperlinks in multimedia video data is a subject with growing interest, as demonstrated by recent work undergone in the framework of the Search and Hyperlinking task within the Mediaeval benchmark initiative. In this paper, we compare NLP-based strategies for precise target selection in video hyperlinking exploiting speech material, with the goal of providing hyperlinks from a specified anchor to help information retrieval. We experimentally compare two approaches enabling to select short portions of videos which are relevant and possibly complementary with respect to the anchor. The first approach exploits a bipartite graph relating utterances and words to find the most relevant utterances. The second one uses explicit topic segmentation, whether hierarchical or not, to select the target segments. Experimental results are reported on the Mediaeval 2013 Search and Hyperlinking dataset which consists of BBC videos, demonstrating the interest of hierarchical topic segmentation for precise target selection.
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Anca-Roxana Simon, Camille Guinaudeau, Pascale Sébillot, Guillaume Gravier. Investigating domain-independent NLP techniques for precise target selection in video hyperlinking. ISCA/IEEE Workshop on Speech, Language and Audio in Multimedia, Sep 2014, Penang, Malaysia. ⟨hal-01053698⟩

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