Extracting Social Networks from Literary Fiction
By David K. Elson, Nicholas Dames, K. McKeown. Annual Meeting of the Association for Computational Linguistics, 2010
380 citations recorded by the source corpus.
Subfields: Artificial Intelligence, Literature and Literary Theory
View Paper: https://www.semanticscholar.org/paper/25ee35474712ac015c2dbb7f1d76bb59af7a9922
How researchers see this paper
The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.
In Artificial Intelligence
- Yejin Choi
- Mohit Bansal
- Noah A. Smith
- Luke S. Zettlemoyer
- Mirella Lapata
- Dan Roth
- Percy Liang
- Mohit Iyyer
In Literature and Literary Theory
- Cristian Danescu-Niculescu-Mizil
- Noah A. Smith
- Lillian Lee
- Hal Daumé Iii
- Ted Underwood
- Mohit Iyyer
- David Bamman
- Snigdha Chaturvedi
Citations in the corpus
- Movie Script Summarization as Graph-based Scene Extraction (2015, background): “Elson et al. (2010) analyze 19th century British novels by constructing a conversational network with vertices corresponding to characters and weighted edges corresponding to the amount of conversational interaction.”
- Chameleons in Imagined Conversations: A New Approach to Understanding Coordination of Linguistic Style in Dialogs (2011, background): “For example, one recent project identifies conversational networks in novels, with the goal of evaluating various literary theories (Elson et al., 2010; Elson and McKeown, 2010).”
- Unsupervised Learning of Evolving Relationships Between Literary Characters (2017, methodology): “…networks of characters depicting limited types of relationships such as formality (He, Barbosa, and Kondrak 2013; Krishnan and Eisenstein 2015), volume of interaction (Elson, Dames, and McKeown 2010), participation in social events (Agarwal , Kotalwar, and Rambow 2013; Agarwal et al. 2014; 2013).”
- Story Comprehension for Predicting What Happens Next (2017, background): “networks of characters (Elson et al., 2010; Elson, 2012; Agarwal et al., 2013, 2014; Krishnan and Eisenstein, 2015; Srivastava et al., 2016).”
- A Bayesian Mixed Effects Model of Literary Character (2014, methodology): “To resolve the former to the latter, we largely follow Davis et al. (2003) and Elson et al. (2010): we define a set of initial characters corresponding to each unique character name that is not a subset of another (e.g., Mr. Tom Sawyer ) and deterministically create a set of allowable variants for…”