Unsupervised Text Classification: a Contractual Risk Detection Approach

Vista sencilla de ítem
dc.contributor.advisorJiménez-Rodríguez, Esteban
dc.contributor.authorVillalobos-Ramos, Omar A.
dc.date.accessioned2021-07-16T23:22:04Z
dc.date.available2021-07-16T23:22:04Z
dc.date.issued2020-11
dc.descriptionEnterprise contracting process tends to be tedious when there is thousands of active contracts to manage. The aim of this work was to implement an automatic indexing and information retrieval method in order to classify the semantic structure within contract documents into two classes, risk and non-risk legal language, on the basis of terms contained in new documents further called queries. The technique implemented is term frequency as the transformation procedure for each of the documents and singular-value decomposition to represent such transformations into a set of optimized number of factors. Queries are analyzed as vectors formed from the linear combination of the terms and compared to known documents class with cosine values to determine the nature of the legal language (as risk or non-risk). The result of this work shows that the class detection is possible using the proposed methodology with high relative percentage of accuracy.es_MX
dc.identifier.citationVillalobos-Ramos, O. A. (2020). Unsupervised Text Classification: a Contractual Risk Detection Approach. Trabajo de obtención de grado, Maestría en Ciencia de Datos. Tlaquepaque, Jalisco: ITESO.es_MX
dc.identifier.urihttps://hdl.handle.net/11117/7452
dc.language.isoenges_MX
dc.publisherITESOes_MX
dc.rights.urihttp://quijote.biblio.iteso.mx/licencias/CC-BY-NC-2.5-MX.pdfes_MX
dc.subjectNatural Language Processinges_MX
dc.subjectContractinges_MX
dc.subjectClassificationes_MX
dc.titleUnsupervised Text Classification: a Contractual Risk Detection Approaches_MX
dc.typeinfo:eu-repo/semantics/masterThesises_MX
dc.type.versioninfo:eu-repo/semantics/acceptedVersiones_MX

Archivos

Bloque original
Mostrando 1 - 1 de 1
Miniatura
Nombre:
Omar_Villalobos_Ramos_MScThesis.pdf
Tamaño:
869.64 KB
Formato:
Adobe Portable Document Format
Descripción:
Descargar

Colecciones

DMAF - Trabajos de fin de Maestría en Ciencia de Datos