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|Title: ||Using clustering techniques to identify arguments in legal documents|
|Authors: ||Poudyal, Prakash|
|Issue Date: ||2018|
|Citation: ||P. Quaresma P. Poudyal, T. Gonçalves. Using clustering techniques to identify argu-
ments in legal documents. In Proceedings of the Twelfth International Workshop on Juris-
informatics, JURISIN 2018, Yokohama, Japan, 2018.|
|Abstract: ||A proposal to automatically identify arguments in legal doc-
uments is presented. In this approach, cluster algorithms are applied to
argumentative sentences in order to identify arguments. One potential
problem with this process is that an argumentative sentence belonging to
one specific argument can also simultaneously be part of another, distinct
argument. To address this issue, a Fuzzy c-means (FCM) clustering al-
gorithm was used and the proposed approach was evaluated with a set of
case-law decisions from the European Court of Human Rights (ECHR).
An extensive evaluation of the most relevant and discriminant features
to this task was performed and the obtained results are presented.
In the context of this work two additional algorithms were developed: 1)
the “Distribution of Sentence to the Cluster Algorithm” (DSCA) was de-
veloped to transfer fuzzy membership values (between 0 and 1) generated
by the FCM to a set of clusters; 2) the “Appropriate Cluster Identifi-
cation Algorithm” (ACIA) to evaluate the proposed clusters against the
gold-standard clusters defined by human experts.
The overall results are quite promising and may be the basis for further
research work and extensions.|
|Appears in Collections:||INF - Comunicações - Em Congressos Científicos Internacionais|
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