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Extracting Social Networks from Literary Fiction

TLDR
The method involves character name chunking, quoted speech attribution and conversation detection given the set of quotes, which provides evidence that the majority of novels in this time period do not fit two characterizations provided by literacy scholars.
Abstract
We present a method for extracting social networks from literature, namely, nineteenth-century British novels and serials. We derive the networks from dialogue interactions, and thus our method depends on the ability to determine when two characters are in conversation. Our approach involves character name chunking, quoted speech attribution and conversation detection given the set of quotes. We extract features from the social networks and examine their correlation with one another, as well as with metadata such as the novel's setting. Our results provide evidence that the majority of novels in this time period do not fit two characterizations provided by literacy scholars. Instead, our results suggest an alternative explanation for differences in social networks.

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Proceedings Article

Chameleons in Imagined Conversations: A New Approach to Understanding Coordination of Linguistic Style in Dialogs

TL;DR: It is argued that fictional dialogs offer a way to study how authors create the conversations but don't receive the social benefits (rather, the imagined characters do), and significant coordination across many families of function words in the large movie-script corpus is found.
Proceedings ArticleDOI

A Bayesian Mixed Effects Model of Literary Character

TL;DR: A model that employs multiple effects to account for the influence of extra-linguistic information (such as author) is introduced and it is found that this method leads to improved agreement with the preregistered judgments of a literary scholar, complementing the results of alternative models.
Proceedings ArticleDOI

Feuding Families and Former Friends: Unsupervised Learning for Dynamic Fictional Relationships

TL;DR: A novel unsupervised neural network is presented that incorporates dictionary learning to generate interpretable, accurate relationship trajectories and jointly learns a set of global relationship descriptors as well as a trajectory over these descriptors for each relationship in a dataset of raw text from novels.
Proceedings ArticleDOI

Story Comprehension for Predicting What Happens Next

TL;DR: This paper presents a story comprehension model that explores three distinct semantic aspects: the sequence of events described in the story, its emotional trajectory, and its plot consistency, and uses a hidden variable to weigh the semantic aspects in the context of the story.
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Social Network Analysis of Alice in Wonderland

TL;DR: This paper annotates Lewis Carroll's Alice in Wonderland using a well-defined annotation scheme which has been used in previous computational models that extract social events from news articles and builds novel types of networks in which links between characters are different types of social events.
References
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Proceedings ArticleDOI

Methods for precise named entity matching in digital collections

TL;DR: An interactive system, built within the context of CLiMB project, which permits a user to locate the occurrences of named entities within a given text, and proposes methods to disambiguate intermediate results.
Proceedings Article

Automatic Analysis of Plot for Story Rewriting

TL;DR: A method for automatic plot analysis of narrative texts that uses components of both traditional symbolic analysis of natural language and statistical machine-learning is presented for the story rewriting task.