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From Paul Libbrecht <p...@hoplahup.net>
Subject Re: [ANN] word2vec for Lucene
Date Thu, 20 Nov 2014 17:18:46 GMT
As far as I could tell, word2vec seems more mathematical, which is rather nice.
At least I see more transparent math in the web-page.
Maybe this helps a bit?

SemanticVectors has always rather pleasant for the LSI/LSA-like approach, but precisely this
is mathematically opaque. 
Maybe it's more a question of presentation.

Paul


On 20 nov. 2014, at 16:24, Koji Sekiguchi <koji@r.email.ne.jp> wrote:

> Hi Paul,
> 
> I cannot compare it to SemanticVectors as I don't know SemanticVectors.
> But word vectors that are produced by word2vec have interesting properties.
> 
> Here is the description of the original word2vec web site:
> 
> https://code.google.com/p/word2vec/#Interesting_properties_of_the_word_vectors
> Interesting properties of the word vectors
> It was recently shown that the word vectors capture many linguistic regularities, for
example vector
> operations vector('Paris') - vector('France') + vector('Italy') results in a vector that
is very
> close to vector('Rome'), and vector('king') - vector('man') + vector('woman') is close
to
> vector('queen')
> 
> Thanks,
> 
> Koji
> 
> 
> (2014/11/20 20:01), Paul Libbrecht wrote:
>> Hello Koji,
>> 
>> how would you compare that to SemanticVectors?
>> 
>> paul
>> 
>> On 20 nov. 2014, at 10:10, Koji Sekiguchi <koji@r.email.ne.jp> wrote:
>> 
>>> Hello,
>>> 
>>> It's my pleasure to share that I have an interesting tool "word2vec for Lucene"
>>> available at https://github.com/kojisekig/word2vec-lucene .
>>> 
>>> As you can imagine, you can use "word2vec for Lucene" to extract word vectors
from Lucene index.
>>> 
>>> Thank you,
>>> 
>>> Koji
>>> -- 
>>> http://soleami.com/blog/comparing-document-classification-functions-of-lucene-and-mahout.html
>> 
>> 
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>> 
> 
> 
> -- 
> http://soleami.com/blog/comparing-document-classification-functions-of-lucene-and-mahout.html
> 
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