Deep learning-based lexical character identification in TV series

Author:

Dalla Torre Paola1ORCID,Fantozzi Paolo2ORCID,Naldi Maurizio2ORCID

Affiliation:

1. Department of Social Sciences—Communication, Education and Psychology, LUMSA University , Piazza delle Vaschette 101 , 00193 Rome, Italy

2. Department of Law, Economics, Politics, and Modern Languages, LUMSA University , Via Marcantonio Colonna 19 , 00192 Rome, Italy

Abstract

Abstract 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.

Publisher

Oxford University Press (OUP)

Subject

Computer Science Applications,Linguistics and Language,Language and Linguistics,Information Systems

Cited by 1 articles. 订阅此论文施引文献 订阅此论文施引文献,注册后可以免费订阅5篇论文的施引文献,订阅后可以查看论文全部施引文献

1. Method for Fault Diagnosis and Location of Television Broadcasting Based on Computer Vision;2024 4th International Conference on Neural Networks, Information and Communication (NNICE);2024-01-19

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