Events Matter: Extraction of Events from Court Decisions

Filtz, Erwin and Navas-Loro, María and Santos, Cristiana and Polleres, Axel and Kirrane, Sabrina ORCID: (2020) Events Matter: Extraction of Events from Court Decisions. In: Legal Knowledge and Information Systems. IOS Press. pp. 33-42. ISBN 978-1-64368-151-1

Available under License Creative Commons: Attribution 4.0 International (CC BY 4.0).

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The analysis of court decisions and associated events is part of the daily life of many legal practitioners. Unfortunately, since court decision texts can often be long and complex, bringing all events relating to a case in order, to understand their connections and durations is a time-consuming task. Automated court decision timeline generation could provide a visual overview of what happened throughout a case by representing the main legal events, together with relevant temporal information. Tools and technologies to extract events from court decisions however are still underdeveloped. To this end, in the current paper we compare the effectiveness of three different extraction mechanisms, namely deep learning, conditional random fields, and rule-based method, to facilitate automated extraction of events and their components (i.e., the event type, who was involved, and when it happened). In addition, we provide a corpus of manually annotated decisions of the European Court of Human Rights, which shall serve as a gold standard not only for our own evaluation, but also for the research community for comparison and further experiments.

Item Type: Book Section
Additional Information: Funding: María Navas-Loro5 work was partially supported by a Predoctoral grant from the I+D+i program of the Universidad Politécnica de Madrid. Sabrina Kirrane is funded by the FWF Austrian Science Fund and the Internet Foundation Austria netidee SCIENCE programme.
Keywords: event extraction, named entity recognition, court decisions
Divisions: Departments > Wirtschaftsinformatik u. Operations Mgmt > Wirtschaftsinformatik und Neue Medien
Version of the Document: Published
Depositing User: Gertraud Novotny
Date Deposited: 18 Dec 2020 11:50
Last Modified: 18 Dec 2020 11:50
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