Publicación:
Temporally anchored relation extraction

dc.contributor.authorGarrido, Guillermo
dc.contributor.authorCabaleiro, Bernardo
dc.contributor.authorPeñas Padilla, Anselmo
dc.contributor.authorRodrigo Yuste, Álvaro
dc.date.accessioned2024-05-21T13:03:31Z
dc.date.available2024-05-21T13:03:31Z
dc.date.issued2012-12-08
dc.description.abstractAlthough much work on relation extraction has aimed at obtaining static facts, many of the target relations are actually fluents, as their validity is naturally anchored to a certain time period. This paper proposes a methodological approach to temporally anchored relation extraction. Our proposal performs distant supervised learning to extract a set of relations from a natural language corpus, and anchors each of them to an interval of temporal validity, aggregating evidence from documents supporting the relation. We use a rich graphbased document-level representation to generate novel features for this task. Results show that our implementation for temporal anchoring is able to achieve a 69% of the upper bound performance imposed by the relation extraction step. Compared to the state of the art, the overall system achieves the highest precision reported.es
dc.description.versionversión publicada
dc.identifier.urihttps://hdl.handle.net/20.500.14468/19985
dc.language.isoen
dc.relation.centerE.T.S. de Ingeniería Informática
dc.relation.departmentLenguajes y Sistemas Informáticos
dc.rightsinfo:eu-repo/semantics/openAccess
dc.rights.urihttp://creativecommons.org/licenses/by-nc-nd/4.0
dc.titleTemporally anchored relation extractiones
dc.typeconference proceedingsen
dc.typeactas de congresoes
dspace.entity.typePublication
relation.isAuthorOfPublication1e1b14bc-1284-4aef-908c-bccf31bd055e
relation.isAuthorOfPublication90ababf8-3bd1-44b2-9d12-368f2c6568ac
relation.isAuthorOfPublication.latestForDiscovery1e1b14bc-1284-4aef-908c-bccf31bd055e
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