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A convex relaxation for weakly supervised relation extraction

Edouard Grave 1, 2, 3
2 SIERRA - Statistical Machine Learning and Parsimony
DI-ENS - Département d'informatique de l'École normale supérieure, Inria Paris-Rocquencourt, CNRS - Centre National de la Recherche Scientifique : UMR8548
Abstract : A promising approach to relation extraction, called weak or distant supervision, exploits an existing database of facts as training data, by aligning it to an unlabeled collection of text documents. Using this approach, the task of relation extraction can easily be scaled to hundreds of different relationships. However, distant supervision leads to a challenging multiple instance, multiple label learning problem. Most of the proposed solutions to this problem are based on non-convex formulations, and are thus prone to local minima. In this article, we propose a new approach to the problem of weakly supervised relation extraction, based on discriminative clustering and leading to a convex formulation. We demonstrate that our approach outperforms state-of-the-art methods on the challenging dataset introduced by Riedel et al. (2012).
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https://hal.inria.fr/hal-01080310
Contributor : Edouard Grave <>
Submitted on : Wednesday, November 5, 2014 - 12:52:15 AM
Last modification on : Tuesday, May 4, 2021 - 2:06:02 PM

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  • HAL Id : hal-01080310, version 1

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Edouard Grave. A convex relaxation for weakly supervised relation extraction. Conference on Empirical Methods in Natural Language Processing (EMNLP), ACL SIGDAT, Oct 2014, Doha, Qatar. ⟨hal-01080310⟩

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