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The Story Of Computational Narratology

3 NARRATIVE UNDERSTANDING

 
Narrative structure understanding is the key to construct computational models. While narrative generation is about deriving engaging stories creatively based on a corpus, understanding has subtle applications. Studying corpora of narratives and understanding different aspects is critical to construct a well-defined model. We will first review the use-cases solved by narrative understanding and later delve into general narrative computational models.

3.1 Narrative Understanding use-cases

 
In Elson et al. [8], social networks are extracted from literary fictions, and nineteenth-century British novels and serials. It is a commonly held belief that literary works represent the social world of the time when the novel was written. By studying dialogue patterns in novels social networks is derived and with that the characterization of novels. In Danescu-Niculescu-Mizil and Lee [4], fictional literary works are studied to understand the coordination of linguistic style in dialogues. Often during conversations we tend to adapt to each others style, which is termed as Chameleon effect. These kind of studies are helpful in understanding social networks.

In Alharthi et al. [1], a recommender system for books based on the content is proposed. It is observed reading books by people has reduced during recent times. Book reading is statistically known to promote better mental health and particularly reading fictional books stimulate profound social communication. There are two approaches to recommender systems: Collaborative Filtering (CF), which are based on ratings given by other readers, and Content Based (CB) recommendations based on the actual content of the book. In situations where we do not have sufficient ratings on a book which is more often, content based systems are more useful.

In Serban et al. [22], a comprehensive survey of copora to build data driven dialog systems is presented. Dialogue systems are very useful in machine to human communication. Till the recent past these systems were built based on expert knowledge and significant engineering. With availability of exhaustive data-sets in public domain, state-of-art computational power and new machine learning models like neural network architectures, there are immense possibilities of building dialogue systems from corpora.

In Ashok et al. [2], a quantitative analysis of successful novels based on writing style is proposed. The answer to whether a novel will be successful or not has been a qualitative approach through expert opinion and experience of publishers. In this work various aspects of style are quantified and used to analyze successful from unsuccessful novels backed by empirical data of prominence from popular book selling websites.

In He et al. [12], a supervised machine learning approach for attributing utterances to the characters in a novel is proposed. Relating utterances to a character is considerably challenging even for a human reader. The solution has applications like generating high-quality audio books without human networks and analysis of dialogues is useful for the study of social interactions.

Writing essays is an important part of education for children. In Somasundaran et al. [23], an automatic way of analyzing narrative quality written by children is proposed. Assessment of different quality dimensions of a written narrative is non-obvious, since creative ways children can use to write are unbounded. With U.S. Common Core State Standards rubric 942 essays were annotated and inter-annotator agreement was used to understand the reliability of scoring.

Above were some example use-cases that were addressed using narrative modelling. In next section, we will review core narrative models.

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