Unsupervised discovery of domain-specific knowledge from text

Hovy, Dirk, Zhang, Chunliang, Hovy, Eduard H. y Peñas, Anselmo(2011) .Unsupervised discovery of domain-specific knowledge from text. .En: . ()

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Titulo Unsupervised discovery of domain-specific knowledge from text
Autor(es) Hovy, Dirk
Zhang, Chunliang
Hovy, Eduard H.
Peñas, Anselmo
Materia(s) Informática
Resumen Learning by Reading (LbR) aims at enabling machines to acquire knowledge from and reason about textual input. This requires knowledge about the domain structure (such as entities, classes, and actions) in order to do inference. We present a method to infer this implicit knowledge from unlabeled text. Unlike previous approaches, we use automatically extracted classes with a probability distribution over entities to allow for context-sensitive labeling. From a corpus of 1.4m sentences, we learn about 250k simple propositions about American football in the form of predicateargument structures like “quarterbacks throw passes to receivers”. Using several statistical measures, we show that our model is able to generalize and explain the data statistically significantly better than various baseline approaches. Human subjects judged up to 96.6% of the resulting propositions to be sensible. The classes and probabilistic model can be used in textual enrichment to improve the performance of LbR end-to-end systems.
Fecha 2011-06-19
Formato application/pdf
Identificador http://e-spacio.uned.es/fez/view/bibliuned:DptoLSI-ETSI-MA2VICMR-1060
Idioma eng
Fuente Proceedings of the 49th Annual Meeting of the Association for Computational Linguistics, pages 1466–1475,Portland, Oregon, June 19-24, 2011
Versión de la publicación publishedVersion
Relacionado con el proyecto: info:eu-repo/grantAgreement/S2009/TIC-1542
Nivel de acceso y licencia http://creativecommons.org/licenses/by-nc-nd/4.0
Tipo de recurso lecture
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Creado: Tue, 25 Nov 2014, 15:19:27 CET