Using citation analysis techniques for computer-assisted legal research in continental jurisdictions

The following research investigates the use of citation analysis techniques for relevance ranking in computer-assisted legal research systems. Overviews on information retrieval, legal research, computer-assisted legal research (CALR), and the role of citations in legal research enable the formulation of a proposition: Relevance ranking in contemporary CALR systems could profit from the use of citation analysis techniques. After examining potential previous work in the areas of Web search, legal network analysis, and legal citation analysis, the proposition is further developed into a testable hypothesis: A basic citation-based algorithm, despite all its shortcomings, could be used to significantly improve relevance ranking in computer-assisted legal research. By computing and analysing the distribution of 242,078 headnote citations across 80,195 opinions written by the Austrian Supreme Court of Justice between 1985 and 2008, proof for this hypothesis is presented

What Matters In On-Policy Reinforcement Learning? A Large-Scale Empirical Study

In recent years, on-policy reinforcement learning (RL) has been successfully applied to many different continuous control tasks. While RL algorithms are often conceptually simple, their state-of-the-art implementations take numerous low- and high-level design decisions that strongly affect the performance of the resulting agents. Those choices are usually not extensively discussed in the literature, leading to discrepancy between published descriptions of algorithms and their implementations. This makes it hard to attribute progress in RL and slows down overall progress [Engstrom'20]. As a step towards filling that gap, we implement >50 such ``choices'' in a unified on-policy RL framework, allowing us to investigate their impact in a large-scale empirical study. We train over 250'000 agents in five continuous control environments of different complexity and provide insights and practical recommendations for on-policy training of RL agents

What Matters In On-Policy Reinforcement Learning? A Large-Scale Empirical Study

International audienceIn recent years, on-policy reinforcement learning (RL) has been successfully applied to many different continuous control tasks. While RL algorithms are often conceptually simple, their state-of-the-art implementations take numerous low- and high-level design decisions that strongly affect the performance of the resulting agents. Those choices are usually not extensively discussed in the literature, leading to discrepancy between published descriptions of algorithms and their implementations. This makes it hard to attribute progress in RL and slows down overall progress [Engstrom'20]. As a step towards filling that gap, we implement >50 such ``choices'' in a unified on-policy RL framework, allowing us to investigate their impact in a large-scale empirical study. We train over 250'000 agents in five continuous control environments of different complexity and provide insights and practical recommendations for on-policy training of RL agents

Confrontational scavenging as a possible source for language and cooperation

The emergence of language and the high degree of cooperation found among humans seems to require more than a straightforward enhancement of primate traits. Some triggering episode unique to human ancestors was likely necessary. Here it is argued that confrontational scavenging was such an episode. Arguments for and against an established confrontational scavenging niche are discussed, as well as the probable effects of such a niche on language and co-operation. Finally, several possible directions for future research are suggested

Proceedings of LOAIT '07 : II Workshop on Legal Ontologies and Artificial Intelligence Techniques

Proceedings of the 2nd Workshop on Legal Ontologies and Artificial Intelligence Techniques June 4th, 2007, Stanford Universit

Zersplitterung von Rechtsdatenbanken und Probleme bei der Vermittlung von Informationskompetenz

Fragmentation of online legal resources and problems in teaching information literacy. Texts from Austrian legal periodicals and annotated codes become scattered over more and more online legal resources. Teaching information literacy therefore becomes more and more challenging for libraries. The authors look at different aspects of this issue and discuss possible solutions

Briefbüchlein für Sonntagsschulen : eine Sammlung belehrender Briefe aus dem Kreise der heranwachsenden Jugend und dem bürgerlichen Leben zum Lesenlernen verschiedener Handschriften und zur Vermehrung der Fertigkeit im Briefschreiben : mit einem Kärtchen von Deutschland

von Joseph Ant. Geist, Schullehrer in Kempte

Legal Query Expansion using Ontologies and Relevance Feedback

Abstract. The aim of our research is the improvement of Boolean search with query expansion using lexical ontologies and user feedback. User studies strongly suggest that standard search techniques have to be improved in order to meet legal particularities. Query expansion can exploit the potential of linguistic knowledge and successful user behaviour. First tentative results show the feasibility of our approach. A first search prototype has been built and tested in the area of European state aid law