Removing Noisy Mentions for Distant Supervision

  1. Intxaurrondo, Ander
  2. Surdeanu, Mihai
  3. López de Lacalle Lecuona, Oier
  4. Agirre Bengoa, Eneko
Revista:
Procesamiento del lenguaje natural

ISSN: 1135-5948

Año de publicación: 2013

Número: 51

Páginas: 41-48

Tipo: Artículo

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Otras publicaciones en: Procesamiento del lenguaje natural

Resumen

Relation Extraction methods based on Distant Supervision rely on true tuples to retrieve noisy mentions, which are then used to train traditional supervised relation extraction methods. In this paper we analyze the sources of noise in the mentions, and explore simple methods to lter out noisy mentions. The results show that a combination of mention frequency cut-o , Pointwise Mutual Information and removal of mentions which are far from the feature centroids of relation labels is able to signi cantly improve the results of two relation extraction models.

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Fuente de los datos: Dialnet