Identification of Case Content with Quantitative Network Analysis: An Example from the ECtHR
Publikation: Bidrag til bog/antologi/rapport › Konferencebidrag i proceedings › Forskning › fagfællebedømt
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Identification of Case Content with Quantitative Network Analysis : An Example from the ECtHR. / Christensen, Martin Lolle; Olsen, Henrik Palmer; Tarissan, Fabian.
Legal Knowledge and Information Systems. red. / Floris Bex; Serena Villata. Amsterdam : IOS Press, 2016. s. 53-62.Publikation: Bidrag til bog/antologi/rapport › Konferencebidrag i proceedings › Forskning › fagfællebedømt
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TY - GEN
T1 - Identification of Case Content with Quantitative Network Analysis
AU - Christensen, Martin Lolle
AU - Olsen, Henrik Palmer
AU - Tarissan, Fabian
N1 - Conference code: XXIX
PY - 2016
Y1 - 2016
N2 - What is a case decided by the European Court of Human Rights about? The Courts own case database, HUDOC, lists all the articles mentioned in a specific case in their metadata. They also supply a number of keywords, but these keywords for the most part are reduced to repeating phrases from the relevant articles. In order to enhance information retrieval about case content, without relying on manual labor and subjective judgment, we propose in this paper a quantitative method that gives a better indication of case content in terms of which articles a given case is more closely associated with. To do so, we rely on the network structure induced by existing case-to-case and case-to-article citations and propose two computational approaches (referred to as MAININ and MAINOUT) which result in assigning one representative article to each case. We validate the approach by selecting a sample of important cases and comparing manual investigation of real content of those cases with the MAININ and MAINOUT articles. Results show that MAININ in particular is able to infer correctly the real content in most of the cases.
AB - What is a case decided by the European Court of Human Rights about? The Courts own case database, HUDOC, lists all the articles mentioned in a specific case in their metadata. They also supply a number of keywords, but these keywords for the most part are reduced to repeating phrases from the relevant articles. In order to enhance information retrieval about case content, without relying on manual labor and subjective judgment, we propose in this paper a quantitative method that gives a better indication of case content in terms of which articles a given case is more closely associated with. To do so, we rely on the network structure induced by existing case-to-case and case-to-article citations and propose two computational approaches (referred to as MAININ and MAINOUT) which result in assigning one representative article to each case. We validate the approach by selecting a sample of important cases and comparing manual investigation of real content of those cases with the MAININ and MAINOUT articles. Results show that MAININ in particular is able to infer correctly the real content in most of the cases.
U2 - 10.3233/978-1-61499-726-9-53
DO - 10.3233/978-1-61499-726-9-53
M3 - Article in proceedings
SN - 978-1-61499-725-2
SP - 53
EP - 62
BT - Legal Knowledge and Information Systems
A2 - Bex, Floris
A2 - Villata, Serena
PB - IOS Press
CY - Amsterdam
Y2 - 14 December 2016 through 16 December 2016
ER -
ID: 170337685