Knowledge capture and textual inference

Cabaleiro Barciela, Bernardo. (2014). Knowledge capture and textual inference Master Thesis, Universidad Nacional de Educación a Distancia (España). Escuela Técnica Superior de Ingeniería Informática. Departamento de Lenguajes y Sistemas Informáticos

Ficheros (Some files may be inaccessible until you login with your e-spacio credentials)
Nombre Descripción Tipo MIME Size
CABALEIRO_BARCIELA_Bernardo_TFM.pdf CABALEIRO_BARCIELA_Bernardo_TFM.pdf application/pdf 1.10MB

Título Knowledge capture and textual inference
Autor(es) Cabaleiro Barciela, Bernardo
Abstract The present and future information needs of the society rely on the ability of computers to understand and manage knowledge. The lack of this mechanism explains the problems of knowledge driven systems to effectively perform tasks as question answering and machine reading. One of the biggest bottlenecks is the automatic knowledge acquisition problem. In the actual stage of development, it seems obvious that only semisupervised or unsupervised techniques can scale to deal with large corpora of natural language like the Web. The trend has evolved from populating a predefined ontology to expressing knowledge through either unconstrained relations or propositions. The arrival of new deep language processing technologies let us think that we can annotate large collections of text with accurate predicates that can be used to extracting knowledge from text without tying it to any predefined logical schema. On the other hand, it is not clear which tasks can harness this knowledge and how it can be done. This master’s thesis proposes a new method of knowledge capture and textual inference based on three cornerstones: (1) First, we develop a procedure to turn plain text into a graph based representation taking advantage of existing tools. (2) Second, we develop a proposition extraction system. (3) Lastly, we study an unsupervised method for correction of appositive dependencies, as an example of the textual inferences that the generated proposition store enables. In addition, we generate two useful resources for future tasks of natural language processing: A corpus of 7 million documents represented as semantically enriched graphs and a proposition store of semantic classes with 8 million instances of entity-class relations.
Notas adicionales Trabajo Final de Máster Universitario en Lenguajes y Sistemas Informáticos. UNED
Materia(s) Informática
Editor(es) Universidad Nacional de Educación a Distancia (España). Escuela Técnica Superior de Ingeniería Informática. Departamento de Lenguajes y Sistemas Informáticos
Director/Tutor Peñas Padilla, Anselmo
Fecha 2014-02-27
Formato application/pdf
Identificador bibliuned:master-ETSInformatica-LSI-Bcabaleiro
Idioma eng
Versión de la publicación acceptedVersion
Nivel de acceso y licencia
Tipo de recurso master Thesis
Tipo de acceso Acceso abierto

Versión Tipo de filtro
Contador de citas: Google Scholar Search Google Scholar
Estadísticas de acceso: 193 Visitas, 85 Descargas  -  Estadísticas en detalle
Creado: Fri, 08 Oct 2021, 23:02:31 CET