A Benchmark of Rule-Based and Neural Coreference Resolution in Dutch Novels and News
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 diffe... Mehr ...
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| Dokumenttyp: | contributionToPeriodical |
| Erscheinungsdatum: | 2020 |
| Verlag/Hrsg.: |
Association for Computational Linguistics (ACL)
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| Sprache: | Englisch |
| Permalink: | https://search.fid-benelux.de/Record/base-29443572 |
| Datenquelle: | BASE; Originalkatalog |
| Powered By: | BASE |
| Link(s) : | https://hdl.handle.net/11370/33f9913e-2c3d-43a0-be45-dcc3a9f62975 |
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