Using Linguistic Information and Machine Learning Techniques to Identify Entities from Juridical Documents

Information extraction from legal documents is an important and open problem. A mixed approach, using linguistic information and machine learning techniques, is described in this paper. In this approach, top-level legal concepts are identified and used for document classifica- tion using Support Vector Machines. Named entities, such as, locations, organizations, dates, and document references, are identified using se- mantic information from the output of a natural language parser. This information, legal concepts and named entities, may be used to popu- late a simple ontology, allowing the enrichment of documents and the creation of high-level legal information retrieval systems. The proposed methodology was applied to a corpus of legal documents - from the EUR-Lex site – and it was evaluated. The obtained results were quite good and indicate this may be a promising approach to the legal information extraction problem

Improving Hazard Classification through the Reuse of Descriptive Arguments

Descria0M e arguments are an itriLM' part of the process ofdetermix2L the dependabi8' y of any system,parti0L6PXM i n the case of safetycriP'60 systems. For such systems, safety cases are constructed to demonstrate that a system meets dependabiPX yrequi0x2X ts. Thi processiss0X8M theappliAA0x2 of hazardanalysi techniL78' However, such techniX8P are error-prone,tir consumir and apply "ad hoc" reuse. Hence, the use ofsystemati' exhausti e hazardanalysi can lead to ani0X'272 ofhiM confidencei the parent dependabiXM y argument thati compromi0x by lack of riM82A We have i vestiL70x theappliLX0xL of structure and reuse techni2MX toi0X7 ve hazard classiL7PL0x arguments andthei associo0L parent dependabi26 y arguments. A structure for hazard arguments has been presented and an example from a software hazardanalysi has been exempliMX usii XML. Usi0 two methods of structural reuse, hazard arguments can beiML2 ved for both argumentgenerati8 and post argumentconstructiP analysic

Extending jCOLIBRI for Textual CBR

Abstract. This paper summarises our work in textual Case-Based Reasoning within jCOLIBRI. We use Information Extraction techniques to annotate web pages to facilitate semantic retrieval over the web. Similarity matching techniques from CBR are applied to retrieve from these annotated pages. We demonstrate the applicability of these extensions by annotating and retrieving documents on the web.

Investigating Graphs in Textual Case-Based Reasoning

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