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Knowledge capture and textual inference

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dc.contributor.authorCabaleiro Barciela, Bernardo
dc.contributor.directorPeñas Padilla, Anselmo
dc.date.accessioned2024-05-20T12:39:33Z
dc.date.available2024-05-20T12:39:33Z
dc.date.issued2014-02-27
dc.description.abstractThe 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.en
dc.description.versionversión final
dc.identifier.urihttps://hdl.handle.net/20.500.14468/14688
dc.language.isoen
dc.publisherUniversidad Nacional de Educación a Distancia (España). Escuela Técnica Superior de Ingeniería Informática. Departamento de Lenguajes y Sistemas Informáticos
dc.relation.centerE.T.S. de Ingeniería Informática
dc.relation.degreeMáster universitario en Investigación en Ingeniería de Software y Sistemas Informáticos
dc.relation.departmentLenguajes y Sistemas Informáticos
dc.rightsinfo:eu-repo/semantics/openAccess
dc.rights.urihttps://creativecommons.org/licenses/by-nc-nd/4.0/deed.es
dc.titleKnowledge capture and textual inferencees
dc.typetesis de maestríaes
dc.typemaster thesisen
dspace.entity.typePublication
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