The MARCELL Legislative Corpus
14568Authors
nan
Abstract
AbstractThis article presents the current outcomes of the MARCELL CEF Telecom project aiming to collect and deeply annotate a large comparable corpus of legal documents. The MARCELL corpus includes 7 monolingual sub-corpora (Bulgarian, Croatian, Hungarian, Polish, Romanian, Slovak and Slovenian) containing the total body of respective national legislative documents. These sub-corpora are automatically sentence split, tokenized, lemmatized and morphologically and syntactically annotated. The monolingual sub-corpora are complemented by a thematically related parallel corpus (Croatian-English). The metadata and the annotations are uniformly provided for each language specific sub-corpus. Besides the standard morphosyntactic analysis plus named entity and dependency annotation, the corpus is enriched with the IATE and EUROVOC labels. The file format is CoNLL-U Plus Format, containing the ten columns specific to the CoNLL-U format and four extra columns specific to our corpora. The MARCELL corpora represents a rich and valuable source for further studies and developments in machine learning, cross-lingual terminological data extraction and classification.
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Proceedings of the 12th Conference on Language Resources and Evaluation (LREC 2020), pages 3761–3768 Marseille, 11–16 May 2020 c⃝ European Language Resources Association (ELRA), licensed under CC-BY-NC 3761 The MARCELL Legislative Corpus Tamás Váradi1, Svetla Koeva2, Martin Yalamov2, Marko Tadić3, Bálint Sass1, Bartłomiej Nitoń4, Maciej Ogrodniczuk4, Piotr Pęzik5, Verginica Barbu Mititelu6, Radu Ion6, Elena Irimia6, Maria Mitrofan6, Vasile Păiș6, Dan Tufiș6, Radovan Garabík7, Simon Krek8, Andraž Repar8, Matjaž Rihtar8, Janez Brank8 1Research Institute for Linguistics, Budapest, Hungary, {varadi.tamas,sass.balint}@nytud.hu 2IBL, BAS, Sofia, Bulgaria, {svetla,martin}@dcl.bas.bg 3University of Zagreb, Faculty of Humanities and Social Sciences, Zagreb, Croatia, marko.tadic@ffzg.hr 4Institute of Computer Science, Polish Academy of Sciences, Warsaw, Poland, bartek.niton@gmail.com, maciej.ogrodniczuk@ipipan.waw.pl 5University of Łódź, Poland, pezik@uni.lodz.pl 6RACAI, Bucharest, Romania, {vergi,radu,elena,maria,vasile,tufis}@racai.ro 7Ľ. Štúr Institute of Linguistics, Slovak Academy of Sciences, Bratislava, Slovakia, garabik@kassiopeia.juls.savba.sk 8IJS, Ljubljana, Slovenia, {simon.krek,matjaz.rihtar,janez.brank}@ijs.si, andraz.repar@cjvt.si Abstract This article presents the current outcomes of the MARCELL CEF Telecom project aiming to collect and deeply annotate a large comparable corpus of legal documents. The MARCELL corpus includes 7 monolingual sub-corpora (Bulgarian, Croatian, Hungarian, Polish, Romanian, Slovak and Slovenian) containing the total body of respective national legislative documents. These sub-corpora are automatically sentence split, tokenized, lemmatized and morphologically and syntactically annotated. The monolingual sub-corpora are complemented by a thematically related parallel corpus (Croatian-English). The metadata and the annotations are uniformly provided for each language specific sub-corpus. Besides the standard morphosyntactic analysis plus named entity and dependency and/or noun phrase annotation, the corpus is enriched with the IATE and EuroVoc labels. The file format is CoNLL-U Plus Format, containing the ten columns specific to the CoNLL-U format and four extra columns specific to our corpora. The MARCELL corpora represent a rich and valuable source for further studies and developments in machine learning, cross-lingual terminological data extraction and classification. Keywords: law corpus, comparable corpus, under-resourced languages 1. Introduction The present paper introduces the MARCELL corpus and related resources compiled in the CEF Telecom1 Action of the same name. The CEF Telecom project Multilingual Resources for CEF.AT in the Legal Domain (MARCELL)2 aims to enhance the eTranslation system3 developed by the European Commission through supplying seven large scale corpora consisting of national legislative documents effective in Bulgaria, Croatia, Hungary, Poland, Romania, Slovakia and Slovenia. The structure of the paper is as follows. We describe the rationale and objectives of the work in section 2. The composition of the corpus in each of the seven languages is presented in 3, whereas section 4 introduces the Croatian̶ English parallel corpus, also created in the project. The format and annotation, as well as the metadata of the multilingual corpora are described in sections 5 and 6 respectively. In some languages the corpus is already enriched with annotation of terminology in IATE4 and EuroVoc5 as described in section 7, the annotation work is still in progress for the rest of the languages. The multilingual corpus will be subdivided into several sub- domains corresponding to top-level categories of the 1https://ec.europa.eu/inea/en/connecting-europe-facility/cef- telecom 2 http://marcell-project.eu 3https://ec.europa.eu/cefdigital/wiki/display/CEFDIGITAL/eTra nslation 4 https://iate.europa.eu/home EuroVoc system. This work is briefly …
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AbstractThis article presents the current outcomes of the MARCELL CEF Telecom project aiming to collect and deeply annotate a large comparable corpus of legal documents. The MARCELL corpus includes 7 monolingual sub-corpora (Bulgarian, Croatian, Hungarian, Polish, Romanian, Slovak and Slovenian) containing the total body of respective national legislative documents. These sub-corpora are automatically sentence split, tokenized, lemmatized and morphologically and syntactically annotated. The monolingual sub-corpora are complemented by a thematically related parallel corpus (Croatian-English). The metadata and the annotations are uniformly provided for each language specific sub-corpus. Besides the standard morphosyntactic analysis plus named entity and dependency annotation, the corpus is enriched with the IATE and EUROVOC labels. The file format is CoNLL-U Plus Format, containing the ten columns specific to the CoNLL-U format and four extra columns specific to our corpora. The MARCELL corpora represents a rich and valuable source for further studies and developments in machine learning, cross-lingual terminological data extraction and classification.
Publication Details
- Paper ID:
- 14568
- Year:
- 2020
- Publication:
- TIPSTER
- Publication Type:
- conf
- DOI:
- nan