A Benchmark of Rule-Based and Neural Coreference Resolution in Dutch Novels and News

• URL: http://arxiv.org/abs/2011.01615v1
• Date: Tue, 3 Nov 2020 10:52:00 GMT
• Title: A Benchmark of Rule-Based and Neural Coreference Resolution in Dutch Novels and News
• Authors: Corb\`en Poot, Andreas van Cranenburgh
• Abstract summary: The results provide insight into the relative strengths of data-driven and knowledge-driven systems. The neural system performs best on news/Wikipedia text, while the rule-based system performs best on literature.
• Score: 4.695687634290403
• License: http://creativecommons.org/licenses/by/4.0/
• 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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