@inproceedings{filt-etal-2020JURIX,
  author    = {Erwin Filtz and
               Mar{\'{\i}}a Navas{-}Loro and
               Cristiana Santos and
               Axel Polleres and
               Sabrina Kirrane},
  editor    = {Villata Serena and
               Jakub Harasta and
               Petr Kremen},
  title     = {Events Matter: Extraction of Events from Court Decisions},
  abstract = {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.},  
  booktitle = {Legal Knowledge and Information Systems - {JURIX} 2020: The Thirty-third
               Annual Conference, Brno, Czech Republic, December 9-11, 2020},
  series    = {Frontiers in Artificial Intelligence and Applications},
  volume    = {334},
  pages     = {33--42},
  publisher = {{IOS} Press},
  year      = {2020},
  url       = {https://doi.org/10.3233/FAIA200847},
  doi       = {10.3233/FAIA200847},
}

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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.","booktitle":"Legal Knowledge and Information Systems - JURIX 2020: The Thirty-third Annual Conference, Brno, Czech Republic, December 9-11, 2020","series":"Frontiers in Artificial Intelligence and Applications","volume":"334","pages":"33–42","publisher":"IOS Press","year":"2020","url":"https://doi.org/10.3233/FAIA200847","doi":"10.3233/FAIA200847","bibtex":"@inproceedings{filt-etal-2020JURIX,\n author = {Erwin Filtz and\n Mar{\\'{\\i}}a Navas{-}Loro and\n Cristiana Santos and\n Axel Polleres and\n Sabrina Kirrane},\n editor = {Villata Serena and\n Jakub Harasta and\n Petr Kremen},\n title = {Events Matter: Extraction of Events from Court Decisions},\n abstract = {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.}, \n booktitle = {Legal Knowledge and Information Systems - {JURIX} 2020: The Thirty-third\n Annual Conference, Brno, Czech Republic, December 9-11, 2020},\n series = {Frontiers in Artificial Intelligence and Applications},\n volume = {334},\n pages = {33--42},\n publisher = {{IOS} Press},\n year = {2020},\n url = {https://doi.org/10.3233/FAIA200847},\n doi = {10.3233/FAIA200847},\n}\n\n","author_short":["Filtz, E.","Navas-Loro, M.","Santos, C.","Polleres, A.","Kirrane, S."],"editor_short":["Serena, V.","Harasta, J.","Kremen, P."],"key":"filt-etal-2020JURIX","id":"filt-etal-2020JURIX","bibbaseid":"filtz-navasloro-santos-polleres-kirrane-eventsmatterextractionofeventsfromcourtdecisions-2020","role":"author","urls":{"Paper":"https://doi.org/10.3233/FAIA200847"},"metadata":{"authorlinks":{"polleres, a":"https://bibbase.org/show?bib=www.polleres.net/mypublications.bib"}},"downloads":0,"html":""},"bibtype":"inproceedings","biburl":"www.polleres.net/mypublications.bib","creationDate":"2021-02-22T22:50:03.680Z","downloads":0,"keywords":[],"search_terms":["events","matter","extraction","events","court","decisions","filtz","navas-loro","santos","polleres","kirrane"],"title":"Events Matter: Extraction of Events from Court Decisions","year":2020,"dataSources":["cBfwyqsLFQQMc4Fss","gixxkiKt6rtWGoKSh","QfLT6siHZuHw9MqvK"]}