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SUMMARY:Predicting Judicial Decisions of the European Court of Human Right
 s - Nikos Aletras (Sheffield)
DTSTART:20180215T110000Z
DTEND:20180215T120000Z
UID:TALK101065@talks.cam.ac.uk
CONTACT:Dimitri Kartsaklis
DESCRIPTION:Recent advances in Natural Language Processing and Machine Lea
 rning provide us with the tools to build predictive models that can be use
 d to unveil patterns driving judicial decisions. This can be useful\, for 
 both lawyers and judges\, as an assisting tool to rapidly identify cases a
 nd extract patterns which lead to certain decisions. This paper presents t
 he first systematic study on predicting the outcome of cases tried by the 
 European Court of Human Rights based solely on textual content. We formula
 te a binary classification task where the input of our classifiers is the 
 textual content extracted from a case and the target output is the actual 
 judgment as to whether there has been a violation of an article of the con
 vention of human rights. Textual information is represented using contiguo
 us word sequences\, i.e.\, N-grams\, and topics. Our models can predict th
 e court’s decisions with a strong accuracy (79% on average). Our empiric
 al analysis indicates that the formal facts of a case are the most importa
 nt predictive factor. This is consistent with the theory of legal realism 
 suggesting that judicial decision-making is significantly affected by the 
 stimulus of the facts. We also observe that the topical content of a case 
 is another important feature in this classification task and explore this 
 relationship further by conducting a qualitative analysis.
LOCATION:Boardroom\, Faculty of English\, West Road
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