
The Amazing Power of Word Vectors (2016) - SamuelKillin
https://blog.acolyer.org/2016/04/21/the-amazing-power-of-word-vectors/
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iconvalleysil
More interesting information from a DOD research lab that resulted in vectors
[https://www.kaggle.com/c/word2vec-nlp-
tutorial/discussion/12...](https://www.kaggle.com/c/word2vec-nlp-
tutorial/discussion/12349)

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charlescearl
Department of Energy, not to be confused with Department of Defense. Granted
there is a lot of nuclear weapons work at DOE, but LBNL does mostly big
science work up the hill from Berkeley

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j_s
Recently HN featured a podcast interview with "one of the creators of Word2Vec
and fastText", documenting some of his professional history.

[https://news.ycombinator.com/item?id=13630678](https://news.ycombinator.com/item?id=13630678)

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Bioeye
This is an awesome explanation of those papers! Does anyone have any cool
examples of word2vec being used in a project? I'd be interested in seeing what
people could make with it.

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mabbo
Document type classification. We wanted to predict which of these k classes a
new text document was.

We trained 100-dim word vectors on all the text content we currently have,
plus some 30,000 wiki articles related to the business. New content comes in,
convert words to vecs, average them, and use that resulting vec as the input
to a basic classifier.

For how simple that is, the method is unreasonably good. Widely applicable
too.

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ideonexus
For anyone looking for a simple javascript explorable explanation of this you
can quickly download and run in a browser, I just found the following GitHub
Project.

Demo:

[http://turbomaze.github.io/word2vecjson/](http://turbomaze.github.io/word2vecjson/)

Code:

[https://github.com/turbomaze/word2vecjson](https://github.com/turbomaze/word2vecjson)

The code looks pretty straightforward so I look forward to exploring this
playground of a new and fascinating concept.

~~~
ideonexus
Update: I'm having a lot of fun exploring the relationships between "nerd,"
"geek," "dork," etc. in this demo. : )

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vonnik
Word vectors are great. We've also written about them at length.[0] But any
one interested in word vectors should also be looking at newer ways of
applying neural nets to text. Specifically, convolutional nets with pooling
for time are producing great results for clustering and classification.

[0]
[https://deeplearning4j.org//word2vec](https://deeplearning4j.org//word2vec)

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mustafabisic1
You nailed it with the "German + airlines" example. Up until that point it was
tough to read for a newbie like me. Great blog post

