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Distant Supervision for Relation Extraction via Piecewise Convolutional Neural Networks
時間 2020-12-30
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基於分段卷積神經網絡的遠程監督實體關係抽取算法: 使用遠程監督進行關係提取有兩個問題: 首先,在遠程監督方法中,從已有的知識庫啓發式地與文本對齊,將對齊結果視爲標記數據。然而啓發式對齊並不準確,可能會標記錯誤; 另外則是在典型的方法中,統計模型已應用於特設特徵,而特徵提取過程的產生的噪聲可能會導致性能不佳。 爲了解決這兩個問題,這篇文章提出了一個新的模型,分段卷積神經網絡(PCNN)與多實例學習。
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相關文章
1.
Distant Supervision for Relation Extraction via Piecewise Convolutional Neural Networks
2.
[論文研讀]Distant Supervision for Relation Extraction via Piecewise Convolutional Neural Networks
3.
[EMNLP2015]Distant supervision for Relation Extraction via Piecewise Convolutional Neural Networks
4.
論文筆記:Distant Supervision for Relation Extraction via Piecewise Convolutional Neural Networks
5.
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【論文閱讀筆記PCNN】Distant Supervision for Relation Extraction via Piecewise Convolutional Neural Networks
7.
nlp論文學習(關係抽取)——Distant Supervision for Relation Extraction via Piecewise Convolutional Neural (2015)
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【Part two: Related Work】Relation Extraction with Distant Supervision(DS)
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《GAN Driven Semi-distant Supervision for Relation Extraction》解讀
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