Removing Noisy Mentions for Distant Supervision

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

ISSN: 1135-5948

Datum der Publikation: 2013

Nummer: 51

Seiten: 41-48

Art: Artikel

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Andere Publikationen in: Procesamiento del lenguaje natural

Zusammenfassung

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