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Title: A Straightforward Approach to Narratologically Grounded Character Identification
One of the most fundamental elements of narrative is character: if we are to understand a narrative, we must be able to identify the characters of that narrative. Therefore, character identification is a critical task in narrative natural language understanding. Most prior work has lacked a narratologically grounded definition of character, instead relying on simplified or implicit definitions that do not capture essential distinctions between characters and other referents in narratives. In prior work we proposed a preliminary definition of character that was based in clear narratological principles: a character is an animate entity that is important to the plot. Here we flesh out this concept, demonstrate that it can be reliably annotated (0.78 Cohen’s κ), and provide annotations of 170 narrative texts, drawn from 3 different corpora, containing 1,347 character co-reference chains and 21,999 non-character chains that include 3,937 animate chains. Furthermore, we have shown that a supervised classifier using a simple set of easily computable features can effectively identify these characters (overall F1 of 0.90). A detailed error analysis shows that character identification is first and foremost affected by co-reference quality, and further, that the shorter a chain is the harder it is to effectively identify as a more » character. We release our code and data for the benefit of other researchers « less
Authors:
; ; ;
Award ID(s):
1749917
Publication Date:
NSF-PAR ID:
10220130
Journal Name:
28th International Conference on Computational Linguistics (COLING 2020)
Page Range or eLocation-ID:
6089 to 6100
Sponsoring Org:
National Science Foundation
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