Jointly Learning Author and Annotated Character N-gram Embeddings: A Case Study in Literary Text

Suraj Maharjan, Deepthi Mave, Prasha Shrestha, Manuel Montes, Fabio A. González, Thamar Solorio


Abstract
An author’s way of presenting a story through his/her writing style has a great impact on whether the story will be liked by readers or not. In this paper, we learn representations for authors of literary texts together with representations for character n-grams annotated with their functional roles. We train a neural character n-gram based language model using an external corpus of literary texts and transfer learned representations for use in downstream tasks. We show that augmenting the knowledge from external works of authors produces results competitive with other style-based methods for book likability prediction, genre classification, and authorship attribution.
Anthology ID:
R19-1080
Volume:
Proceedings of the International Conference on Recent Advances in Natural Language Processing (RANLP 2019)
Month:
September
Year:
2019
Address:
Varna, Bulgaria
Venue:
RANLP
SIG:
Publisher:
INCOMA Ltd.
Note:
Pages:
684–692
Language:
URL:
https://www.aclweb.org/anthology/R19-1080
DOI:
10.26615/978-954-452-056-4_080
Bib Export formats:
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PDF:
http://aclanthology.lst.uni-saarland.de/R19-1080.pdf