Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: A Dataset of German Legal Documents for Named Entity Recognition   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.

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The categories of this paper are: quant-ph
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The categories of this paper are: chao-dyn
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The categories of this paper are: hep-th
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The categories of this paper are: adap-org
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The categories of this paper are: atom-ph
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The categories of this paper are: bayes-an
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The categories of this paper are: q-fin
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The categories of this paper are: gr-qc
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The categories of this paper are: math
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The categories of this paper are: comp-gas
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The categories of this paper are: hep-ph
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The categories of this paper are: patt-sol
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The categories of this paper are: dg-ga
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The categories of this paper are: q-bio
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The categories of this paper are: funct-an
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The categories of this paper are: q-alg