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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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Dissertation

Netze, Karten, Irrgärten:Graphenbasierte explorative Ansätze zur Datenanalyse und Anwendungsentwicklung in den Geisteswissenschaften

TL;DR: In den Geisteswissenschaften und angrenzenden Bereichen hat die Digitalisierung Einzug gehalten, wie durch interaktive Visualisiers auch grosen Datenmengen, wieso Wikipedia, zuganglich gemacht werden.

Extracting Character Networks to Explore Literary Plot Dynamics

TL;DR: This paper automatically extracts networks (graphs) of characters for each part of Leo Tolstoy’s novel War and peace using two different techniques for network creation, and evaluates these two techniques against a set of manually created gold standard networks.
Journal ArticleDOI

Error Analysis in an Automated Narrative Information Extraction Pipeline

TL;DR: This paper presents their method for automatically extracting narrative information of characters and their narrative roles from natural language stories and presents an analytical methodology to study how the error is introduced by different modules and how it propagates through the pipeline.
Posted Content

Measuring Information Propagation in Literary Social Networks

TL;DR: In this article, the authors present the task of modeling information propagation in literature, in which they seek to identify pieces of information passing from character A to character B to character C, only given a description of their activity in text.
Proceedings Article

AttitudeMiner: Mining Attitude from Online Discussions

TL;DR: This demonstration presents AttitudeMiner, a system for mining attitude from online discussions using linguistic techniques to analyze the text exchanged between participants of online discussion threads at different levels of granularity to identify the polarity of the attitude the discussants carry towards one another.
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.
Book

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.