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Distributed Representations of Words and Phrases and their Compositionality--翻譯
時間 2021-01-02
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詞和短語的分佈表示及其構成 原作者:Tomas Mikolov等 #摘要 最近引入的連續Skip-gram模型是學習高質量分佈向量表示的有效方法,分佈向量表示可以捕獲大量精確的句法和語義關係。在本文中,我們提出了幾個擴展,提高了向量的質量和訓練速度。通過對頻繁的單詞進行二次抽樣,我們獲得了顯着的加速,同時還學習了更多的常規單詞表示。我們還提出了一個分層soft max的簡單替代方案,稱爲負採樣。
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相關文章
1.
Distributed Representations of Words and Phrases and their Compositionality
2.
翻譯:Distributed Representations of Words and Phrases and their Compositionality
3.
Distributed Representations of Words and Phrases and their Compositionality翻譯與感悟
4.
Distributed Representations of Words and Phrases and their Compositionality論文閱讀及實戰
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(29)[NIPs13] Distributed Representations of Words and Phrases and their Compositionality
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《Distributed Representations of Words and Phrases and their Compositionality》筆記
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論文《Distributed Representations of words and Phrase and their Compositionality》
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Distributed Representations of Sentences and Documents
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NLP-文獻-Distributed Representations of Sentences and Documents
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