Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely:   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 beten the nouns detected in the video transcripts and a bag of nouns generated from WordNet, for each category proposed to classify the videos. Specifically,  have used the Lin measure based on WordNet, which calculates the semantic distance beten 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,  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  have shown that, using this combination of semantic similarity and information retrieval,  can improve the results obtained using the information retrieval approach only.
['José M. Perea-Ortega', 'Arturo Montejo Ráez', 'Manuel Carlos Díaz-Galiano', 'Maria Teresa Martín-Valdivia']
I am writing a research paper on a idea Genre tagging of videos based on information retrieval and semantic similarity using WordNet
[{"from": "human", "value": "I am writing a research paper on a idea Genre tagging of videos based on information retrieval and semantic similarity using WordNet"}, {"from": "gpt", "value": "Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely:   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 beten the nouns detected in the video transcripts and a bag of nouns generated from WordNet, for each category proposed to classify the videos. Specifically,  have used the Lin measure based on WordNet, which calculates the semantic distance beten 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,  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  have shown that, using this combination of semantic similarity and information retrieval,  can improve the results obtained using the information retrieval approach only."}]