LEADER 04395nam 22006375 450 001 9910586636903321 005 20230810232749.0 010 $a981-19-2928-9 024 7 $a10.1007/978-981-19-2928-1 035 $a(MiAaPQ)EBC7073243 035 $a(Au-PeEL)EBL7073243 035 $a(CKB)24429519400041 035 $a(DE-He213)978-981-19-2928-1 035 $a(PPN)264193059 035 $a(EXLCZ)9924429519400041 100 $a20220812d2022 u| 0 101 0 $aeng 135 $aurcnu|||||||| 181 $ctxt$2rdacontent 182 $cc$2rdamedia 183 $acr$2rdacarrier 200 10$aAnalysis of Legal Argumentation Documents $eA Computational Argumentation Approach /$fby Hayato Hirata, Katsumi Nitta 205 $a1st ed. 2022. 210 1$aSingapore :$cSpringer Nature Singapore :$cImprint: Springer,$d2022. 215 $a1 online resource (162 pages) 225 1 $aTranslational Systems Sciences,$x2197-8840 ;$v29 311 08$aPrint version: Hirata, Hayato Analysis of Legal Argumentation Documents Singapore : Springer,c2022 9789811929274 327 $aChapter 1. Introduction -- Chapter 2. Related Research -- Chapter 3 -- Factor-Based Argumentation Evaluation -- Chapter 4. Case Analysis Using Factors and Legal Topoi -- Chapter 5. Case Analysis for Case Law Education using Factors and Computational Argumentation Theory -- Chapter 6. Conclusion. 330 $aThis book introduces methods to analyze legal documents such as negotiation records and legal precedents, using computational argumentation theory. First, a method to automatically evaluate argumentation skills from the records of argumentation exercises is proposed. In law school, argumentation exercises are often conducted and many records of them are produced. From each utterance in the record, a pattern of ?speech act +factor? is extracted, and argumentation skills are evaluated from the sequences of the patterns, using a scoring prediction model constructed by multiple regression analyses between the appearance pattern and the scoring results. The usefulness of this method is shown by applying it to the example case ?the garbage house problem?. Second, a method of extracting factors (elements that characterize precedents and cases) and legal topoi from individual precedents and using them as the expression of precedents to analyze how the pattern of factors and legal topoi appearing in a group of precedents affects the judgment (plaintiff wins/defendant wins) is proposed. This method has been applied to a group of tax cases. Third, the logical structure of 70 labor cases is described in detail by using factors and a bipolar argumentation framework (BAF) and an (extended argumentation framework (EAF) together. BAF describes the logical structure between plaintiff and defendant, and EAF describes the decision of the judge. Incorporating the legal topoi into the EAF of computational argumentation theory, the strength of the analysis of precedents by combined use of factored BAF and EAF, not only which argument the judge adopted could be specified. It was also possible to determine what kind of value judgment was made and to verify the logic. The analysis methods in this book demonstrate the application of logic-based AI methods to the legal domain, and they contribute to the education and training of law school students in logical ways of argumentation. 410 0$aTranslational Systems Sciences,$x2197-8840 ;$v29 606 $aSocial legislation 606 $aLaw$xPhilosophy 606 $aLaw$xHistory 606 $aArtificial intelligence 606 $aLabour Law/Social Law 606 $aTheories of Law, Philosophy of Law, Legal History 606 $aPhilosophy of Law 606 $aArtificial Intelligence 615 0$aSocial legislation. 615 0$aLaw$xPhilosophy. 615 0$aLaw$xHistory. 615 0$aArtificial intelligence. 615 14$aLabour Law/Social Law. 615 24$aTheories of Law, Philosophy of Law, Legal History. 615 24$aPhilosophy of Law. 615 24$aArtificial Intelligence. 676 $a343.0999 700 $aHirata$b Hayato$01253437 702 $aNitta$b Katsumi 801 0$bMiAaPQ 801 1$bMiAaPQ 801 2$bMiAaPQ 906 $aBOOK 912 $a9910586636903321 996 $aAnalysis of Legal Argumentation Documents$92905958 997 $aUNINA