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Báo cáo khoa học: "Can Document Selection Help Semi-supervised Learning? A Case Study On Event Extraction"

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Annotating training data for event extraction is tedious and labor-intensive. Most current event extraction tasks rely on hundreds of annotated documents, but this is often not enough. In this paper, we present a novel self-training strategy, which uses Information Retrieval (IR) to collect a cluster of related documents as the resource for bootstrapping.

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Nội dung Text: Báo cáo khoa học: "Can Document Selection Help Semi-supervised Learning? A Case Study On Event Extraction"

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