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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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Journal ArticleDOI

Story embedding: Learning distributed representations of stories based on character networks

TL;DR: The embedding model with the first approach ‘flow-oriented Story2Vec’ can reflect the context and flow of stories if the dynamics of character networks is well understood and can emphasize the denouement of stories, which is an overview of the static structure of the character networks.
Proceedings Article

Toward automatic role identification in unannotated folk tales

TL;DR: An approach for automatically identifying high-level narrative structure information, particularly character roles, from unannotated folk tales by introducing a new representation called action matrices to encode Propp's narrative theory on character role and their "sphere of action".
Journal ArticleDOI

Towards Evaluating Narrative Quality In Student Writing

TL;DR: This work lays the foundation for automated assessments of narrative quality in student writing by first manually score essays for narrative-relevant traits and sub-traits, and measuring inter-annotator agreement to build an automated scoring system.
Proceedings ArticleDOI

Validating Literary Theories Using Automatic Social Network Extraction

TL;DR: A closer reading of the theories themselves is conducted, a revised and expanded set of hypotheses based on a divergent interpretation of the conspiracy theories are presented, and the scope of networks is widened for validating this expandedSet of hypotheses.
Proceedings Article

A Dictionary of Wisdom and Wit: Learning to Extract Quotable Phrases

TL;DR: This paper evaluates quotable phrase extraction using a large digital library and demonstrates that an integration of lexical and shallow syntactic features results in a reliable extraction process.
References
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Journal ArticleDOI

A Coefficient of agreement for nominal Scales

TL;DR: In this article, the authors present a procedure for having two or more judges independently categorize a sample of units and determine the degree, significance, and significance of the units. But they do not discuss the extent to which these judgments are reproducible, i.e., reliable.
Proceedings ArticleDOI

Incorporating Non-local Information into Information Extraction Systems by Gibbs Sampling

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TL;DR: As a brilliant survey of English literature in terms of changing attitudes towards country and city, Williams' highly-acclaimed study reveals the shifting images and associations between these two traditional poles of life throughout the major developmental periods of English culture.
Proceedings Article

The Automatic Content Extraction (ACE) Program Tasks, Data, and Evaluation

TL;DR: The objective of the ACE program is to develop technology to automatically infer from human language data the entities being mentioned, the relations among these entities that are directly expressed, and the events in which these entities participate.
Book

Graphs, Maps, Trees: Abstract Models for a Literary History

TL;DR: MoreMoretti as discussed by the authors argues that literature scholars should stop reading books and start counting, graphing, and mapping them instead, and offers charts, maps and time lines, developing the idea of "distant reading" into a full-blown experiment in literary history.