Difference between revisions of "word2vec-like System"

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=== 2013 ===
 
=== 2013 ===
 
* https://code.google.com/p/word2vec/
 
* https://code.google.com/p/word2vec/
** [[word2vec System|This tool]] provides an efficient implementation of the [[continuous bag-of-words]] and [[skip-gram architecture]]s for computing [[vector representations of words]]. These representations can be subsequently used in many [[natural language processing application]]s and for further research.        <P>        ...        <P>        The [[word2vec System|word2vec tool]] takes a [[text corpus]] as input and produces the [[word vectors]] as output. It first constructs a [[vocabulary]] from the [[training text data]] and then [[learns vector representation of words]]. The resulting [[word2vec Model|word vector file]] can be used as features in many [[NLP application|natural language processing]] and [[machine learning application]]s.        <P>        A simple way to investigate the learned representations is to find the closest [[word]]s for a [[user-specified]] [[word]]. The [[word2vec distance|distance tool]] serves that purpose. For example, if you enter 'france', distance will display the most similar words and their distances to 'france', which should look like ...  
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** [[word2vec-like System|This tool]] provides an efficient implementation of the [[continuous bag-of-words]] and [[skip-gram architecture]]s for computing [[vector representations of words]]. These representations can be subsequently used in many [[natural language processing application]]s and for further research.        <P>        ...        <P>        The [[word2vec-like System|word2vec tool]] takes a [[text corpus]] as input and produces the [[word vectors]] as output. It first constructs a [[vocabulary]] from the [[training text data]] and then [[learns vector representation of words]]. The resulting [[word2vec Model|word vector file]] can be used as features in many [[NLP application|natural language processing]] and [[machine learning application]]s.        <P>        A simple way to investigate the learned representations is to find the closest [[word]]s for a [[user-specified]] [[word]]. The [[word2vec distance|distance tool]] serves that purpose. For example, if you enter 'france', distance will display the most similar words and their distances to 'france', which should look like ...  
 
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Revision as of 20:45, 23 December 2019

A word2vec-like System is a distributional word embedding training system that applies a word2vec algorithm (based on work by Tomáš Mikolov, Kai Chen, Greg Corrado, Jeffrey Dean, et al[1]).



References

2015

2014

2014

2013

2013b

2013a