Department of Software Technology
Vienna University of Technology
The Exploration of Legal Text Corpora with Hierarchical Neural Networks:
A Guided Tour in Public International Law
The classification of feature vectors representing the
interpretation of legal documents improves the search for
similar or related documents, the interpretation of these
documents as well as the navigation within the text corpus.
The need for effective approaches of classification is
dramatically increased nowadays due to the advent of
massive digital libraries containing free-form legal text
documents. What we are looking for are powerful methods
for the exploration of such libraries whereby the detection
of similarities between groups of documents is the overall
goal. In other words, methods that may be used to gain
insight in the inherent structure of the various items
contained in a text archive are needed.
In this paper we present the results from a case study in
legal document classification based on an experimental
document archive comprising important treaties in public
international law. The core task of classification is
performed by a non-standard neural network model with a
layered architecture consisting of mutually independent
unsupervised neural networks. The distinguished features of
this learning architecture is the remarkably fast training
time combined with the benefit of explicit cluster
representation. The access to legal text archives may be
enhanced by guided tours providing the means for
convenient voyage in an environment of dynamically
classified legal documents.
Up
Comments: rauber@ifs.tuwien.ac.at