A Benchmark of Rule-Based and Neural Coreference Resolution in Dutch Novels and News
November 03, 2020 Β· Entered Twilight Β· π CRAC
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Repo contents: .gitignore, .travis.yml, LICENSE, README.md, e2edutch, requirements.txt, scripts, setup.py, test
Authors
Corbèn Poot, Andreas van Cranenburgh
arXiv ID
2011.01615
Category
cs.CL: Computation & Language
Citations
18
Venue
CRAC
Repository
https://github.com/andreasvc/crac2020
β 4
Last Checked
1 month ago
Abstract
We evaluate a rule-based (Lee et al., 2013) and neural (Lee et al., 2018) coreference system on Dutch datasets of two domains: literary novels and news/Wikipedia text. The results provide insight into the relative strengths of data-driven and knowledge-driven systems, as well as the influence of domain, document length, and annotation schemes. The neural system performs best on news/Wikipedia text, while the rule-based system performs best on literature. The neural system shows weaknesses with limited training data and long documents, while the rule-based system is affected by annotation differences. The code and models used in this paper are available at https://github.com/andreasvc/crac2020
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