HARAŠTA, Jakub and Jaromír ŠAVELKA. Toward Linking Heterogenous References in Czech Court Decisions to Content. In Adam Wyner, Giovanni Casini. Frontiers in Artificial Intelligence and Applications , vol. 302 (Proceedings of JURIX 2017). Amsterdam - Berlin - Washington, DC: IOS Press. p. 177-182. ISBN 978-1-61499-838-9. doi:10.3233/978-1-61499-838-9-177. 2017.
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Basic information
Original name Toward Linking Heterogenous References in Czech Court Decisions to Content
Authors HARAŠTA, Jakub (203 Czech Republic, guarantor, belonging to the institution) and Jaromír ŠAVELKA (203 Czech Republic).
Edition Amsterdam - Berlin - Washington, DC, Frontiers in Artificial Intelligence and Applications , vol. 302 (Proceedings of JURIX 2017), p. 177-182, 6 pp. 2017.
Publisher IOS Press
Other information
Original language English
Type of outcome Proceedings paper
Field of Study 50501 Law
Country of publisher Netherlands
Confidentiality degree is not subject to a state or trade secret
Publication form electronic version available online
WWW Open access sborníku
RIV identification code RIV/00216224:14220/17:00095267
Organization unit Faculty of Law
ISBN 978-1-61499-838-9
Doi http://dx.doi.org/10.3233/978-1-61499-838-9-177
UT WoS 000450475100023
Keywords in English case law analysis; reference recognition; conditional random fields; information extraction
Tags rivok
Tags International impact, Reviewed
Changed by Changed by: JUDr. Mgr. Jakub Harašta, Ph.D., učo 323070. Changed: 2/1/2019 10:08.
Abstract
In this paper we present initial results from our effort to automatically detect references in decisions of the courts in the Czech Republic and link these references to their content. We focus on references to case-law and legal literature. To deal with wide variety in how references are expressed we use a novel distributed approach to reference recognition. Instead of attempting to recognize the references as a whole we focus on their lower level constituents. We assembled a corpus of 350 decisions and annotated it with more than 50,000 annotations corresponding to different reference constituents. Here we present our first attempt to detect these constituents automatically
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GA17-20645S, research and development projectName: Exaktní hodnocení aplikační relevance judikatury
Investor: Czech Science Foundation
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