Hierarchical Topic Models for Language-based Video Hyperlinking

Anca-Roxana Simon (1) , Rémi Bois (1) , Guillaume Gravier (1) , Pascale Sébillot (1) , Emmanuel Morin (2) , Sien Moens (3)

1 LinkMedia - Creating and exploiting explicit links between multimedia fragments
2 LS2N - équipe TALN - Traitement Automatique du Langage Naturel
3 Department of Computer Science - K.U.Leuven

We investigate video hyperlinking based on speech transcripts , leveraging a hierarchical topical structure to address two essential aspects of hyperlinking, namely, serendipity control and link justification. We propose and compare different approaches exploiting a hierarchy of topic models as an intermediate representation to compare the transcripts of video segments. These hierarchical representations offer a basis to characterize the hyperlinks, thanks to the knowledge of the topics who contributed to the creation of the links, and to control serendipity by choosing to give more weights to either general or specific topics. Experiments are performed on BBC videos from the Search and Hyperlinking task at MediaEval. Link precisions similar to those of direct text comparison are achieved however exhibiting different targets along with a potential control of serendipity.

Keywords: NLP; Multimedia; topic modeling; hyperlinking

Domains: Multimedia [cs.MM]; Document and Text Processing; Information Retrieval [cs.IR]

Anca-Roxana Simon, Rémi Bois, Guillaume Gravier, Pascale Sébillot, Emmanuel Morin, et al.. Hierarchical Topic Models for Language-based Video Hyperlinking. Workshop on Speech, Language and Audio in Multimedia, 2015, Brisbane, Australia. ⟨10.1145/2802558.2814642⟩. ⟨hal-01186429⟩