Methods For Automatically Generating a Legal Thesaurus

dc.contributor.advisor	Hendrickx, I.H.E.
dc.contributor.advisor	Kunneman, F.A.
dc.contributor.author	Vos, Hugo P. de
dc.date.issued	2017-08-31
dc.description.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.	en_US
dc.identifier.uri	http://theses.ubn.ru.nl/handle/123456789/5027
dc.language.iso	en	en_US
dc.thesis.faculty	Faculteit der Letteren	en_US
dc.thesis.specialisation	Researchmaster Language and Communication	en_US
dc.thesis.studyprogramme	Researchmasters	en_US
dc.thesis.type	Researchmaster	en_US
dc.title	Methods For Automatically Generating a Legal Thesaurus	en_US

Now showing 1 - 1 of 1