论文标题
荷兰小说和新闻中基于规则和神经核心决议的基准
A Benchmark of Rule-Based and Neural Coreference Resolution in Dutch Novels and News
论文作者
论文摘要
我们评估了一个基于规则的（Lee等，2013）和Neural（Lee等，2018）的核心系统在两个领域的荷兰数据集上：文学小说和新闻/Wikipedia文本。结果提供了对数据驱动和知识驱动系统的相对优势的见解，以及域，文档长度和注释方案的影响。神经系统在新闻/Wikipedia文本上表现最好，而基于规则的系统在文献上表现最好。神经系统具有有限的培训数据和长文档的弱点，而基于规则的系统则受注释差异的影响。本文使用的代码和模型可在https://github.com/andreasvc/crac2020上找到
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
