Word2vec（二）：Overview

Word2vec Overview

2020/12/27

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https://pixabay.com/zh/photos/coffee-grinds-filter-2616923/

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◎ Abstract

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◎ Introduction

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本論文要解決（它之前研究）的（哪些）問題（弱點）？ 

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# NNLM。

NNLM 雖然有很好的詞表示，但是訓練複雜度比較大。

https://www.jiqizhixin.com/graph/technologies/c61ba3b9-40e2-4864-a941-9adc19e6792e

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◎ Method

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解決方法？ 

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# Word2vec 1。

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具體細節？

http://hemingwang.blogspot.com/2021/07/word2vecillustrated.html

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◎ Result

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本論文成果。 

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◎ Discussion

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本論文與其他論文（成果或方法）的比較。 

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成果比較。 

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方法比較。 

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◎ Conclusion 

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◎ Future Work

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後續相關領域的研究。 

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後續延伸領域的研究。

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◎ References

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# NNLM。被引用 7185 次。

Bengio, Yoshua, et al. "A neural probabilistic language model." Journal of machine learning research 3.Feb (2003): 1137-1155.

http://www-labs.iro.umontreal.ca/~felipe/IFT6010-Automne2011/resources/tp3/bengio03a.pdf

# Word2vec 1。被引用 18991 次。

Mikolov, Tomas, et al. "Efficient estimation of word representations in vector space." arXiv preprint arXiv:1301.3781 (2013).

https://arxiv.org/pdf/1301.3781.pdf

# Word2vec 2。被引用 23990 次。

Mikolov, Tomas, et al. "Distributed representations of words and phrases and their compositionality." Advances in neural information processing systems. 2013.

https://papers.nips.cc/paper/2013/file/9aa42b31882ec039965f3c4923ce901b-Paper.pdf

# Word2vec 3。被引用 645 次。

Rong, Xin. "word2vec parameter learning explained." arXiv preprint arXiv:1411.2738 (2014).

https://arxiv.org/pdf/1411.2738.pdf

# C&W v1。被引用 5099 次。

Collobert, Ronan, and Jason Weston. "A unified architecture for natural language processing: Deep neural networks with multitask learning." Proceedings of the 25th international conference on Machine learning. 2008.

http://www.cs.columbia.edu/~smaskey/CS6998-Fall2012/supportmaterial/colbert_dbn_nlp.pdf

# C&W v2。被引用 6841 次。本篇論文闡釋了從 Word2vec 繼續發展 Paragraph2vec 的必要性。

Collobert, Ronan, et al. "Natural language processing (almost) from scratch." Journal of machine learning research 12.ARTICLE (2011): 2493-2537.

https://www.jmlr.org/papers/volume12/collobert11a/collobert11a.pdf

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The Star Also Rises: Word2vec

https://hemingwang.blogspot.com/2019/04/word2vec.html

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