Genre tagging of videos based on information retrieval and semantic similarity using WordNet

In this paper we propose a new approach for the genre tagging task of videos, using only their ASR transcripts and associated metadata. This new approach is based on calculating the semantic similarity between the nouns detected in the video transcripts and a bag of nouns generated from WordNet, for each category proposed to classify the videos. Specifically, we have used the Lin measure based on WordNet, which calculates the semantic distance between two synsets. Obviously, this approach has been only applied on the English test videos due to the use of WordNet, an English lexical resource. As base case, we have applied an information retrieval system as a classifier, using the generated bag of nouns for each category as index data and the ASR transcripts from each test video as query. Several experiments have been submitted, one of them combining both approaches (information retrieval and semantic similarity). As main conclusion we have shown that, using this combination of semantic similarity and information retrieval, we can improve the results obtained using the information retrieval approach only.