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A large German Legal Corpus of laws, administrative regulations and court decisions issued in Germany at federal level. Query the corpus: corpora.dipintra.it

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GeLeCo

A large German Legal Corpus of laws, administrative regulations and court decisions issued in Germany at federal level

Documentation

1. Introduction

GeLeCo is a large German Legal Corpus for research, teaching and translation purposes. It includes the complete collection of federal laws, administrative regulations and court decisions published on three online databases by the German Federal Ministry of Justice and Consumer Protection and the Federal Office of Justice (www.gesetze-im-internet.de, www.verwaltungsvorschriften-im-internet.de, www.rechtsprechung-im-internet.de). The corpus is publicly accessible on the website of the Department of Interpreting and Translation of the University of Bologna, Forlì campus (corpora.dipintra.it).

2. Corpus design

2.1. Composition

text type database URL text count token count
laws gesetze-im-internet.de 6,567 23,030,293
court decisions rechtsprechung-im-internet.de 55,361 169,569,142
administrative regulations verwaltungsvorschriften-im-internet.de 767 3,508,284
total count 62,695 196,107,719

The largest subcorpus (the corpus of court decisions published on www.rechtsprechung-im-internet.de) has the following composition:

issuing court text count %
Bundesarbeitsgericht (BAG) 5,697 10,3%
Bundesfinanzhof (BFH) 8,964 16,2%
Bundesgerichtshof (BGH) 19,069 34,4%
Bundespatentgericht (BPatG) 5,913 10,7%
Bundessozialgericht (BSG) 4,460 8,1 %
Bundesverfassungsgericht (BVerfG) 3,878 7,0%
Bundesverwaltungsgericht (BVerwG) 7,189 13,9%
NA 191 0,3%
total 55,361 100,0%

2.2. Annotation scheme

The corpus was compiled in vertical or word-per-line (WPL) format as required by SketchEngine and NoSketchEngine and marked-up with contextual (metadata), structural (text and sentence boundaries) and linguistic (POS tagging, lemmatisation) annotation (s. below). Tokenization, POS tagging and lemmatization were carried out using the TreeTagger (tagset).

<corpus>
<text type="Gerichtsentscheidung" level="Bund" title="GmbH: Beschränkung der Stimmrechtsausübungsfreiheit eines Gesellschafters aufgrund der Treuepflicht" title_abbreviation="NA" drafting_date="12.04.2016" decade="2010" database_URL="rechtsprechung-im-internet.de" court="BGH" court_detail="BGH 2. Zivilsenat" reference="II ZR 275/14" year="2016" decision_type="Urteil" ECLI="ECLI:DE:BGH:2016:120416UIIZR275.14.0">
<s>
Die	ART	die
Berufung	NN	Berufung
der	ART	die
Klägerin	NN	Klägerin
gegen	APPR	gegen
das	ART	die
Urteil	NN	Urteil
der	ART	die
Kammer	NN	Kammer
für	APPR	für
Handelssachen	NN	Handelssache
des	ART	die
Landgerichts	NN	Landgericht
Ingolstadt	NE	Ingolstadt
vom	APPRART	von+die
15.	ADJA	@ord@
Oktober	NN	Oktober
2013	CARD	@card@
wird	VAFIN	werden
zurückgewiesen	VVPP	zurückweisen
.	$.	.
</s>
</text>
</corpus>

2.3. Metadata

Contextual information annotated for each text includes:

  • title
  • title_abbreviation
  • type: can take one of the following values: Gesetz (law), Gerichtsentscheidung (court decision), Verwaltungsvorschrift (administrative regulation)
  • level: indicates whether the law, regulation or court decision was published at federal or Länder level. This metadatum was included with sight to a possible extension of the corpus to laws, regulations and court decisions published at Länder level. It can take the following values:
    • Bund: federal level
    • Land: Länder level (not present in this corpus)
  • drafting_date: this corresponds to the Ausfertigungsdatum of laws and the Entscheidungsdatum of court decisions.
  • year
  • decade
  • database_URL: can take the following values: gesetze-im-internet.de, rechtsprechung-im-internet.de, verwaltungsvorschriften-im-internet.de
  • court
  • court_detail
  • reference: a reference code for court decisions (Aktenzeichen)
  • decision_type: the type of document for court decisions (Dokumenttyp)
  • ECLI: the European Case Law Identifier code for court decisions.

3. Corpus building steps

3.1. URL collection

All URLs were collected by means of website-specific web scrapers written in Python. Three lists of URLs were exported in newline-separated .txt files for subsequent text scraping.

3.2. Text scraping and XML tagging

Based on the previously collected URL lists, single legal texts were scraped by means of ad hoc web scrapers written in Python. Text and metadata collection was carried out using the BeautifulSoup Python library. Text contained in different HTML tags was newline-separated, making the subsequent sentence splitting stage easier and faster to carry out. After scraping, texts were merged and a first raw corpus version was exported as a single .txt file for each subcorpus.

3.3. Boilerplate cleaning, deduplication, text filtering

Boilerplate text was eliminated by means of regular expressions. Texts extracted from not correctly visualized webpages (not containing any law, regulation or court decision) were discarded. Texts also underwent a deduplication process based on metadata equivalence.

3.4. Sentence splitting

After scraping and cleaning, the subcorpora were sentence splitted. In particular, only lines containing two or more period characters underwent sentence splitting. For this task, a sentence-splitter based on Koehn and Schroeder's Lingua::Sentence was used, and a list of non-breaking prefixes with legal abbreviations taken from the corpus and from online sources was supplied in order to improve sentence splitting accuracy.

3.5. POS tagging and lemmatization

The corpus was tagged with Part-of-Speech tags and lemmas using the TreeTagger. The output did not undergo any systematic revision or correction stage; therefore, the corpus may contain minor sentence splitting or metadata errors.

4. How to build the corpus

Install all the required dependencies:

pip install -r requirements.txt

To build each subcorpus, run the respective script in the URLscraper folder to collect the URLs, then scrape the texts and metadata using the respective script in the TextMetadataScraper folder. To process the raw subcorpora and build the final corpus in .vert format, run the scripts in the corpus_processing folder in the following order:

  • XMLcorpus_merger_tagger.py
  • corpus-cleaning_sentence-splitting.py
  • deduplicate.py
  • xml2tagged.py
  • tagged2vert.py

Reference

Contarino, A. G. (2021, July 13-17): GeLeCo: a large German Legal Corpus of laws, administrative regulations and court decisions [Poster presentation], 11th International Corpus Linguistics Conference 2021 (CL2021), University of Limerick, Limerick, Ireland

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A large German Legal Corpus of laws, administrative regulations and court decisions issued in Germany at federal level. Query the corpus: corpora.dipintra.it

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