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IJSTR >> Volume 9 - Issue 2, February 2020 Edition

International Journal of Scientific & Technology Research  
International Journal of Scientific & Technology Research

Website: http://www.ijstr.org

ISSN 2277-8616

Framework For Proactive Visualization Of Text Based Narrative Using NLP

[Full Text]



Arnold Gabriel Benedict, Samiksha Shukla



Conditional Batch Normalization (CBN), Natural Language Processing (NLP), Probabilistic Modelling, Part-Of-Speech (POS), Tokenization.



Language is an essential mode, not only for human communication—but also for thinking. A story is conveyed or a report of an incident is being told, humans perceive the conveyed information in the form of visual insights. The increasing advancements in the field of artificial intelligence can help with the same in machines. This paper reflects on the internalization of stories from a cognitive perspective and outlines a scalable framework for supporting the visualization of narrative text data. This paper leverages natural language processing (NLP), probabilistic modelling of discourse knowledge, information extraction of narrative components (who, where, when, what) and the narrative visualization. The graphics knowledge base storage structure has been redesigned to obviate the necessity of having a larger database for all graphics entity. With the developed framework, any user can input unrestricted natural language for the dynamic generation of animated scenes. This provides users with direct visual output in response to their natural language input. This tool can potentially impact the way humans interact with computers and expand a completely new way of understanding conversations.



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