FanfictionNLP: A Text Processing Pipeline for Fanfiction

Michael Yoder, Sopan Khosla, Qinlan Shen, Aakanksha Naik, Huiming Jin, Hariharan Muralidharan, Carolyn Rosé


Abstract
Fanfiction presents an opportunity as a data source for research in NLP, education, and social science. However, answering specific research questions with this data is difficult, since fanfiction contains more diverse writing styles than formal fiction. We present a text processing pipeline for fanfiction, with a focus on identifying text associated with characters. The pipeline includes modules for character identification and coreference, as well as the attribution of quotes and narration to those characters. Additionally, the pipeline contains a novel approach to character coreference that uses knowledge from quote attribution to resolve pronouns within quotes. For each module, we evaluate the effectiveness of various approaches on 10 annotated fanfiction stories. This pipeline outperforms tools developed for formal fiction on the tasks of character coreference and quote attribution
Anthology ID:
2021.nuse-1.2
Volume:
Proceedings of the Third Workshop on Narrative Understanding
Month:
June
Year:
2021
Address:
Virtual
Venues:
NAACL | NUSE
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
13–23
Language:
URL:
https://aclanthology.org/2021.nuse-1.2
DOI:
10.18653/v1/2021.nuse-1.2
Bibkey:
Copy Citation:
PDF:
https://aclanthology.org/2021.nuse-1.2.pdf
Code
 michaelmilleryoder/fanfiction-nlp