word2vec Explained: deriving Mikolov et al.'s negative-sampling word-embedding method · HackerPapers discussions

word2vec Explained: deriving Mikolov et al.'s negative-sampling word-embedding method — mentioned by faizshah

Tue, 14 Nov 2017 19:07:30 GMT

I like Pugh's paper on skip lists [1], Shannon's "Mathematical Theory of Communication" [2], and these two might be a stretch but I also like Rong's "Word2vec Parameter Learning Explained" [3] and Levy & Goldberg's "Word2vec Explained" [4]. In any case use the recommended papers to learn paper reading skills e.g look at references when you don't understand a concept, find an introductory textbook to clarify a proof method, write a summary to make your understanding concrete. Good Luck! [1]: http://courses.cs.vt.edu/cs2604/fall05/wmcquain/Notes/Supple... [2]…

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word2vec Explained: deriving Mikolov et al.'s negative-sampling word-embedding method — mentioned by jventura

Wed, 13 Sep 2017 13:24:54 GMT

Neural networks are all the rage these days. As my knowledge on NNs is quite limited, what I could grasp from the wikipedia article [0] and the original paper [1] is that word2vec uses a vector to represent words in a n-dimensional space. This seems quite similar to LSA (Latent Semantic Analysis). In LSA, a word is represented by a vector (list / array) of values which represent the number of occurrences of the word in a document. For instance, the list [0, 5, 9, 10] for the word "aeroplane" means than "aeroplane" occurs zero times in D1 (document #1), 5 times in D2, etc. Unlike LSA, word2vec…

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word2vec Explained: deriving Mikolov et al.'s negative-sampling word-embedding method — mentioned by denzil_correa

Thu, 02 Mar 2017 10:10:23 GMT

> Words with similar vectors have similar meanings. Can you explain WHY this is the case with word2vec? I have come across a paper which says "we don't know really". Is this true? https://arxiv.org/abs/1402.3722

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word2vec Explained: deriving Mikolov et al.'s negative-sampling word-embedding method — mentioned by denzil_correa

Thu, 02 Mar 2017 10:08:24 GMT

Can someone explain WHY do word vectors with similar contexts club together and are good? One of the papers suggest that "we don't really know" (Section 4) [0] https://arxiv.org/abs/1402.3722

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word2vec Explained: deriving Mikolov et al.'s negative-sampling word-embedding method — mentioned by vonnik

Sat, 03 Oct 2015 19:40:07 GMT

Yoav is one of the smartest guys in this space. He and Omer Levy also wrote a great explanation of word2vec: http://arxiv.org/pdf/1402.3722v1.pdf https://levyomer.wordpress.com/2014/04/25/word2vec-explained...

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word2vec Explained: deriving Mikolov et al.'s negative-sampling word-embedding method — mentioned by syllogism

Wed, 03 Jun 2015 15:45:00 GMT

You might be interested to read this: http://arxiv.org/pdf/1402.3722v1.pdf word2vec Explained: Deriving Mikolov et al.’s Negative-Sampling Word-Embedding Method. Yoav Goldberg and Omer Levy. arXiv 2014. [pdf] The word2vec software of Tomas Mikolov and colleagues has gained a lot of traction lately, and provides state-of-the-art word embeddings. The learning models behind the software are described in two research papers. We found the description of the models in these papers to be somewhat cryptic and hard to follow. While the motivations and presentation may be obvious to the neural-networks…

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word2vec Explained: deriving Mikolov et al.'s negative-sampling word-embedding method — HN submission

Mon, 02 Feb 2015 07:32:50 GMT

Word2Vec Explained [pdf]

1 point; 0 discussion replies on HN. Paper and source history. A submission is not an endorsement.