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

页码：4478 / 4485

页数：8

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