Law JournalFeeds

Abstract  Legal information retrieval is in need of the provision of legal knowledge for the improvement of search strategies. For this
purpose, the LOIS project is concerned with the construction of a multilingual WordNet for cross-lingual information retrieval
in the legal domain. In this article, we set out how a hybrid approach, featuring lexically and legally grounded conceptual
representations, can fit the cross-lingual information retrieval needs of both legal professionals and laymen

Abstract  We describe research carried out as part of a text summarisation project for the legal domain for which we use a new XML corpus
of judgments of the UK House of Lords. These judgments represent a particularly important part of public discourse due to
the role that precedents play in English law. We present experimental results using a range of features and machine learning
techniques for the task of predicting the rhetorical status of sentences and for the task of selecting the most summary-worthy
sentences from a document. Results for these components are encouraging as they achieve state-of-the-art accuracy using robust,
automatically generated cue phrase information. Sample output from the system illustrates the potential of summarisation technology
for legal information management systems and highlights the utility of our rhetorical annotation scheme as a model of legal
discourse, which provides a clear means for structuring summaries and tailoring them to different types of users.

Abstract  Computational machineries dedicated to the attribution of legal responsibility should be based on (or, make use of) a stack
of definitions relating the notion of legal responsibility to a number of suitably chosen causal notions. This paper presents
a general analysis of legal responsibility and of causation in fact based on Hart and Honor�’s work. Some physical aspects
of causation in fact are then treated within the “lite” version of DOLCE foundational ontology written in OWL-DL, a standard
description logic for the Semantic Web.

Abstract  Legislation usually lacks a systematic organization which makes the management and the access to norms a hard problem to face.
A more analytic semantic unit of reference (provision) for legislative texts was identified. A model of provisions (provisions types and their arguments) allows to describe the semantics of rules in legislative texts. It can be used to
develop advanced semantic-based applications and services on legislation. In this paper an automatic bottom-up strategy to
qualify existing legislative texts in terms of provision types is described.

Abstract  Legal codes, such as the Uniform Commercial Code (UCC) examined in this article, are good points of entry for AI and ontology
work because of their more straightforward adaptability to relationship linking and rules-based encoding. However, approaches
relying on encoding solely on formal code structure are incomplete, missing the rich experience of practitioner expertise
that identifies key relationships and decision criteria often supplied by experienced practitioners and process experts from
various disciplines (e.g., sociology, political economics, logistics, operations research). This research focuses on the UCC
because it transcends the limitations of a formal code, functioning essentially as a composite. AI work can benefit from real-world
codes like the UCC, which are essentially formal codes enlightened from a more realistic experience-base from centuries of
development in international commercial transactions settings. This paper then describes our initial work in converting an
expert system on the U.S. law governing the sale of goods from Article II of the Uniform Commercial Code (UCC), into a knowledge-based
system using the Web Ontology Language OWL.