Change-Aware Legal Document Retrieval Model
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Date
2009-10-09
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Addis Ababa University
Abstract
Legal documents play a basic role in discharging
the law to the public, besides constituting
learning material for students, researchers and
legal practitioners. Several web portals and
document repositories are hosting legal
documents. Legal documents contain text rich
contents that can be structured and marked with
description languages such as XML. This basic
feature can be exploited by XML based retrieval
models to return legal document parts as a match
for information needs rather than presenting the
entire statue. The main principle behind the XML
based retrieval models is to take the structured
information of documents (as formatted by XML
tags) and the hierarchical document organization
into account when responding to user queries and
when computing the relevance ranking.
Under different paradigms, legal documents are
subjected to change, as the laws they carry
undergo modifications or amendments. The
dynamicity of the law is manifested by the
change in the legal documents, and this change in
turn will have a sever consequence on the
retrieval quality of XML based legal document
retrieval systems. In this work we present a
generic model for a change-aware legal document
retrieval system using XML based IR approach.
Based on the model designed we have developed
a prototype system that demonstrates the validity
of our ideas and algorithms. By identifying the
status of legal contents and by tracking their
relationship, we have achieved an improved
access to legislative contents. In order to realize
this approach we have incorporated the Lucene
searching and indexing APIs and proposed a
novel re-ranking algorithm that considers the
changes in the legal document contents and status
of the law contained.
The evaluation of our system shows a significant
turnover as the result of incorporating the idea of
re-ranking search results. Before re-ranking the
average precision value for the first five results
returned was 46% but after re-ranking the
average precision becomes 82.5% in satisfying
user information needs.
Description
Keywords
Change-Aware Legal Document Retrieval Model, Xml Based Retrieval Model, Legal Document Retrieval, Re-Ranking Search Results in Legal Document Retrieval Systems