Open AccessProceedings Article
Multi-instance Multi-label Learning for Relation Extraction
Mihai Surdeanu,Julie Tibshirani,Ramesh Nallapati,Christopher D. Manning +3 more
- 12 Jul 2012
- pp 455-465
TL;DR: This work proposes a novel approach to multi-instance multi-label learning for RE, which jointly models all the instances of a pair of entities in text and all their labels using a graphical model with latent variables that performs competitively on two difficult domains.
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Abstract: Distant supervision for relation extraction (RE) -- gathering training data by aligning a database of facts with text -- is an efficient approach to scale RE to thousands of different relations. However, this introduces a challenging learning scenario where the relation expressed by a pair of entities found in a sentence is unknown. For example, a sentence containing Balzac and France may express BornIn or Died, an unknown relation, or no relation at all. Because of this, traditional supervised learning, which assumes that each example is explicitly mapped to a label, is not appropriate. We propose a novel approach to multi-instance multi-label learning for RE, which jointly models all the instances of a pair of entities in text and all their labels using a graphical model with latent variables. Our model performs competitively on two difficult domains.
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Distant Supervision for Relation Extraction via Piecewise Convolutional Neural Networks
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Knowledge Graphs
Aidan Hogan,Eva Blomqvist,Michael Cochez,Claudia d'Amato,Gerard de Melo,Claudio Gutierrez,José Emilio Labra Gayo,Sabrina Kirrane,Sebastian Neumaier,Axel Polleres,Roberto Navigli,Axel-Cyrille Ngonga Ngomo,Sabbir M. Rashid,Anisa Rula,Lukas Schmelzeisen,Juan F. Sequeda,Steffen Staab,Antoine Zimmermann +17 more
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•Proceedings Article
Knowledge-Based Weak Supervision for Information Extraction of Overlapping Relations
Raphael Hoffmann,Congle Zhang,Xiao Ling,Luke Zettlemoyer,Daniel S. Weld +4 more
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TL;DR: A novel approach for multi-instance learning with overlapping relations that combines a sentence-level extraction model with a simple, corpus-level component for aggregating the individual facts is presented.
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