Deep learning-based lexical character identification in TV series.

Automated character identification in movies and TV series has been typically carried out through face detection in video and the association of faces with characters' names extracted from dialogues or cast lists. We propose a deep learning architecture to identify characters based on subtitles only...

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Publicado en:Digital Scholarship in the Humanities Vol. 38; no. 4; pp. 1453 - 1466
Autores principales: Torre, Paola Dalla, Fantozzi, Paolo, Naldi, Maurizio
Formato: Artículo
Publicado: Oxford University Press / USA Dec2023
Materias:
Acceso en línea:Ver este registro en EBSCOhost
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        atl: Deep learning-based lexical character identification in TV series.
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          Torre, Paola Dalla
          Fantozzi, Paolo
          Naldi, Maurizio
        affil:
          Department of Social Sciences—Communication, Education and Psychology, LUMSA University , Piazza delle Vaschette 101 , 00193 Rome, Italy
          Department of Law, Economics, Politics, and Modern Languages, LUMSA University , Via Marcantonio Colonna 19 , 00192 Rome, Italy
      su:
        Television series
        Television characters
        Deep learning
      sug:
        subj:
          Television series
          Television characters
          Deep learning
      keyword:
        character identification
        deep learning
        Gomorrah
        lexicons
        TV series
      ab: Automated character identification in movies and TV series has been typically carried out through face detection in video and the association of faces with characters' names extracted from dialogues or cast lists. We propose a deep learning architecture to identify characters based on subtitles only, precisely through the lexicon those characters employ. The identification task is formalized as a multi-class classification task. We apply our technique to the complete set of episodes in the Gomorrah TV series and achieve an average identification accuracy beyond 94 per cent on the full set of characters.
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    language: English
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