Abstract
We consider the task of predicting how literary a text is, with a gold standard from human ratings. Aside from a standard bigram baseline, we apply rich syntactic tree fragments, mined from the training set, and a series of hand-picked features. Our model is the first to distinguish degrees of highly and less literary novels using a variety of lexical and syntactic features, and explains 76.0 % of the variation in literary ratings.- Anthology ID:
- E17-1115
- Volume:
- Proceedings of the 15th Conference of the European Chapter of the Association for Computational Linguistics: Volume 1, Long Papers
- Month:
- April
- Year:
- 2017
- Address:
- Valencia, Spain
- Editors:
- Mirella Lapata, Phil Blunsom, Alexander Koller
- Venue:
- EACL
- SIG:
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 1228–1238
- Language:
- URL:
- https://aclanthology.org/E17-1115
- DOI:
- Cite (ACL):
- Andreas van Cranenburgh and Rens Bod. 2017. A Data-Oriented Model of Literary Language. In Proceedings of the 15th Conference of the European Chapter of the Association for Computational Linguistics: Volume 1, Long Papers, pages 1228–1238, Valencia, Spain. Association for Computational Linguistics.
- Cite (Informal):
- A Data-Oriented Model of Literary Language (van Cranenburgh & Bod, EACL 2017)
- PDF:
- https://preview.aclanthology.org/ingest-acl-2023-videos/E17-1115.pdf