Papers with character identification

4 papers
Improving Automatic Quotation Attribution in Literary Novels (2023.acl-short)

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Challenge: Existing methods for quotation attribution in literary novels require varying levels of available information.
Approach: They propose to train and evaluate models for character identification, coreference resolution, quotation identification and speaker attribution tasks using an annotated dataset.
Outcome: The proposed model scores on speaker attribution task on the same scale as state-of-the-art models.
Frowning Frodo, Wincing Leia, and a Seriously Great Friendship: Learning to Classify Emotional Relationships of Fictional Characters (N19-1)

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Challenge: Existing literature analysis does not focus on roles of characters or on relationships between them.
Approach: They propose to combine emotion and character identification into a unified framework for character network extraction from fictional texts.
Outcome: The proposed task is based on fan-fiction short stories and is able to predict emotion relations in the extracted network graph.
A Straightforward Approach to Narratologically Grounded Character Identification (2020.coling-main)

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Challenge: Existing definitions of character are based on simplified or implicit definitions that do not capture essential distinctions between characters and other referents in narratives.
Approach: They propose a narratologically grounded definition of character that is based on clear narrological principles and annotated 170 narrative texts.
Outcome: The proposed definition of character is based on clear narratological principles and can be reliably annotated (0.78 Cohen’s ).
Character Coreference Resolution in Movie Screenplays (2023.findings-acl)

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Challenge: Movie screenplays have a distinct narrative structure.
Approach: They develop a method to extract structural information and character coreference clusters from movie screenplays by leveraging a movie parser and a character coreferser.
Outcome: The proposed methods scale to long movie screenplays without dramatically increasing their memory footprints.

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