A Dataset of German Legal Documents for Named Entity Recognition

Proceedings of the 12th Conference on Language Resources and Evaluation (LREC 2020), pages 4478–4485 Marseille, 11–16 May 2020 c⃝ European Language Resources Association (ELRA), licensed under CC-BY-NC 4478 A Dataset of German Legal Documents for Named Entity Recognition Elena Leitner, Georg Rehm, Juli´an Moreno-Schneider DFKI GmbH, Alt-Moabit 91c, 10559 Berlin, Germany {firstname.lastname}@dfki.de Abstract 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. Keywords: Named Entity Recognition, NER, Legal Documents, Legal Domain, Corpus Creation, Corpus Annotation 1. Introduction and Motivation Just like any other field, the legal domain is facing multiple challenges in the era of digitisation. Document collections are growing at an enormous pace and their complete and deep analysis can only be tackled with the help of assisting technologies. This is where content curation technologies based on text analytics come in Bourgonje et al. (2016). Such domain-specific semantic technologies enable the fast and efficient automated processing of heterogeneous doc- ument collections, extracting important information units and metadata such as, among others, named entities, nu- meric expressions, concepts and topics, time expressions, and text structure. One of the fundamental processing tasks is the identification and categorisation of named en- tities (Named Entity Recognition, NER). Typically, NER is focused upon the identification of semantic categories such as person, location and organization but, especially in domain-specific applications, other typologies have been developed that correspond to task-, language- or domain- specific needs. With regard to the legal domain, the lack of freely available datasets has been a stumbling block for text analytics research. German newspaper datasets from CoNNL 2003 (Sang and Meulder, 2003) or GermEval 2014 (Benikova et al., 2014) are simply not suitable in terms of domain, text type or semantic categories covered. The work described in this paper was carried out under the umbrella of the project Lynx: Building the Legal Knowl- edge Graph for Smart Compliance Services in Multilingual Europe, a three-year EU-funded project that started in De- cember 2017 (Montiel-Ponsoda et al., 2017).1 Its objec- tive is the creation of a legal knowledge graph that con- tains different types of legal and regulatory data (Schneider and Rehm, 2018a; Schneider and Rehm, 2018b; Moreno- Schneider et al., 2020). Lynx aims to help European com- panies, especially SMEs, that want to become active in new European countries and markets. The project offers compliance-related services that are currently tested and validated in three use cases (UC): (i) UC1 aims to anal- yse contracts, enriching them …

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