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• W2795142274 abstract "Knowledge-based question answering relies on the availability of facts, the majority of which cannot be found in structured sources (e.g. Wikipedia info-boxes, Wikidata). One of the major components of extracting facts from unstructured text is Relation Extraction (RE). In this paper we propose a novel method for creating distant (weak) supervision labels for training a large-scale RE system. We also provide new evidence about the effectiveness of neural network approaches by decoupling the model architecture from the feature design of a state-of-the-art neural network system. Surprisingly, a much simpler classifier trained on similar features performs on par with the highly complex neural network system (at 75x reduction to the training time), suggesting that the features are a bigger contributor to the final performance." @default.
• W2795142274 created "2018-04-06" @default.
• W2795142274 creator A5017772135 @default.
• W2795142274 creator A5026253371 @default.
• W2795142274 date "2018-03-24" @default.
• W2795142274 modified "2023-09-27" @default.
• W2795142274 title "Simple Large-scale Relation Extraction from Unstructured Text" @default.
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• W2795142274 hasPublicationYear "2018" @default.
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