A Dataset of German Legal Documents for Named Entity Recognition

被引：0
|
作者：
Leitner, Elena [1 ]
Rehm, Georg [1 ]
Moreno-Schneider, Julian [1 ]
机构：
[1] DFKI GmbH, Alt Moabit 91c, D-10559 Berlin, Germany
来源：
PROCEEDINGS OF THE 12TH INTERNATIONAL CONFERENCE ON LANGUAGE RESOURCES AND EVALUATION (LREC 2020) | 2020年
基金：
欧盟地平线“2020”;
关键词：
Named Entity Recognition; NER; Legal Documents; Legal Domain; Corpus Creation; Corpus Annotation;
D O I：
暂无
中图分类号：
TP39 [计算机的应用];
学科分类号：
081203 ; 0835 ;
摘要：
We describe a dataset developed for Named Entity Recognition in German federal court decisions. It consists of approx. 67,000 sentences with over 2 million tokens. The resource contains 54,000 manually annotated entities, mapped to 19 fine-grained semantic classes: person, judge, lawyer, country, city, street, landscape, organization, company, institution, court, brand, law, ordinance, European legal norm, regulation, contract, court decision, and legal literature. The legal documents were, furthermore, automatically annotated with more than 35,000 TimeML-based time expressions. The dataset, which is available under a CC-BY 4.0 license in the CoNNL-2002 format, was developed for training an NER service for German legal documents in the EU project Lynx.
引用
收藏
页码：4478 / 4485
页数：8
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