Methods For Automatically Generating a Legal Thesaurus
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2017-08-31
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en
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Abstract
Automatic thesaurus generation is a desired technique for the reason that a thesaurus
is a useful tool in NLP, but manually making a thesaurus is expensive and
time consuming. In this thesis, the process of thesaurus generation is divided up in
two parts: term extraction and relation extraction. Term extraction being the process
of automatically finding candidate terms for a legal thesaurus and relation extraction
is the process of finding which terms are hypernyms of each other. For term extraction
different termhood measures are used: Log Likelihood, Kullback Leibler Divergence
and the measure as assigned by the TExSIS tool. For relation extraction, different
classifiers are trained to classify whether two terms have a hypernym-relation.
The conclusion of this thesis is that no system could be built that can autonomously
build a thesaurus and that in the short term it is better to look for a system to assist
humans in making a thesaurus.
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Faculteit der Letteren