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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
5.
(29)[NIPs13] Distributed Representations of Words and Phrases and their Compositionality
6.
《Distributed Representations of Words and Phrases and their Compositionality》筆記
7.
論文《Distributed Representations of words and Phrase and their Compositionality》
8.
Distributed Representations of Sentences and Documents
9.
NLP-文獻-Distributed Representations of Sentences and Documents
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