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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 ArticleDOI

Frowning Frodo, Wincing Leia, and a Seriously Great Friendship: Learning to Classify Emotional Relationships of Fictional Characters

TL;DR: In this article, a unified framework was proposed to classify emotional relationships of fictional characters, based on character identification and relation classification, with the best result of 0.45 F1 achieved with a GRU with character position indicators on the task of predicting undirected emotion relations in the associated social network graph.
Journal ArticleDOI

Evaluating named entity recognition tools for extracting social networks from novels

TL;DR: There are no significant differences between old and modern novels but that both are subject to a large amount of variance, and this work sees this work as a step in creating more culturally-aware AI systems.
Posted Content

Mining and modeling character networks

TL;DR: Using machine learning techniques based on motif (or small subgraph) counts, it is determined that the Chung-Lu model best fits character networks and it is conjecture why this may be the case.
Proceedings Article

The 54th Annual Meeting of the Association for Computational Linguistics

TL;DR: It is shown that topological fields can be predicted reliably using sequence labeling and that the predicted field labels can inform a transitionbased dependency parser.
Proceedings ArticleDOI

Who Sides with Whom? Towards Computational Construction of Discourse Networks for Political Debates

TL;DR: Three contributions towards the vision of computational construction of discourse networks from newspaper reports are presented, linking the task to knowledge base population and an annotated pilot corpus of migration claims based on German newspaper reports.
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

TL;DR: By using simulated annealing in place of Viterbi decoding in sequence models such as HMMs, CMMs, and CRFs, it is possible to incorporate non-local structure while preserving tractable inference.
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The Country and the City

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.