Legal Text Interpretation: Identifying Hohfeldian Relations from Text

Legal Text Interpretation: Identifying Hohfeldian Relations from Text Wim Peters1 and Adam Wyner2 1Department of Computer Science, University of Sheffield, Sheffield, United Kingdom w.peters@sheffield.ac.uk 2Department of Computing Science, University of Aberdeen, Aberdeen, United Kingdom azwyner@abdn.ac.uk Abstract The paper investigates the extent of the support semi-automatic analysis can provide for the specific task of assigning Hohfeldian relations of Duty, using the General Architecture for Text Engineering tool for the automated extraction of Duty instances and the bearers of associated roles. The outcome of the analysis supports scholars in identifying Hohfeldian structures in legal text when performing close reading of the texts. A cyclic workflow involving automated annotation and expert feedback will incrementally increase the quality and coverage of the automatic extraction process, and increasingly reduce the amount of manual work required of the scholar. Keywords: legal relations, Hohfeld, text analysis, information extraction 1. Introduction The automatic acquisition of knowledge from text is a com- plex, incomplete and incremental process. Knowledge is quite often available but left implicit in text, or depends on previous analysis steps. Legal experts need tools that help to extract and interpret large amounts of legal texts in a uniform way. Of particular interest, experts wish to access the norms, which express the duties, rights, etc. of the parties discussed in the law. At a more fine-grained level, it is important to access who bears what role with respect to the norm, that is, who is the responsible agent or the receiving party within the action. Yet, it is widely acknowledged that given the complexity of legal language, this is a difficult task. One step towards fa- cilitating this task is to establish a semantic model of the norms, giving the structure of the parties, the roles they play, and the interconnections among the different forms of norms. Such a model provides a target to guide iden- tification and extraction of key textual components, which would move us closer to the goal of making the contents of legal texts accessible in greater detail, variety, and volume. The task we have set ourselves in this paper is to make se- lected, specific semantic content, e.g. the Hohfeldian du- ties, from a sample of European directives explicit and ac- cessible for manual expert interpretation, hypothesis testing and further research. We do this by facilitating the inclusion of language and information technology into legal inter- pretation workflows customised to scholars’ research ques- tions. Also, our wider aim is to provide tools for focused close reading, data interpretation and exploration, collabo- rative annotation and discussion. The work described in this paper investigates the extent of the support semi-automatic analysis can provide for the specific task of assigning Hohfeldian relations, and works towards a principled methodology for computer-assisted in- terpretation of legal texts, on the basis of an evaluated text analytic tool for supporting automated or semi-automated identification and extraction of Hohfeldian knowledge from the source text. The paper is organised as follows. In Section 2., we discuss the theoretical background to this …

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