What does 'Linear regularities among words' mean?












2












$begingroup$


Context: In the paper "Efficient Estimation of Word Representations in Vector Space" by T. Mikolov et al., the authors make use of the phrase: 'Linear regularities among words'.



What does that mean in the context of the paper, or in a general context related to NLP?



Quoting the paragraph from the paper:




Somewhat surprisingly, it was found that similarity of word
representations goes beyond simple syntactic regularities. Using a
word offset technique where simple algebraic operations are performed
on the word vectors, it was shown for example that vector(”King”) -
vector(”Man”) + vector(”Woman”) results in a vector that is closest to
the vector representation of the word Queen [20].



In this paper, we try to maximize accuracy of these vector operations
by developing new model architectures that preserve the linear
regularities among words. We design a new comprehensive test set for
measuring both syntactic and semantic regularities1 , and show that
many such regularities can be learned with high accuracy. Moreover, we
discuss how training time and accuracy depends on the dimensionality
of the word vectors and on the amount of the training data.







nlp language-model representation







share|improve this question









$endgroup$

















    2












    $begingroup$


    Context: In the paper "Efficient Estimation of Word Representations in Vector Space" by T. Mikolov et al., the authors make use of the phrase: 'Linear regularities among words'.



    What does that mean in the context of the paper, or in a general context related to NLP?



    Quoting the paragraph from the paper:




    Somewhat surprisingly, it was found that similarity of word
    representations goes beyond simple syntactic regularities. Using a
    word offset technique where simple algebraic operations are performed
    on the word vectors, it was shown for example that vector(”King”) -
    vector(”Man”) + vector(”Woman”) results in a vector that is closest to
    the vector representation of the word Queen [20].



    In this paper, we try to maximize accuracy of these vector operations
    by developing new model architectures that preserve the linear
    regularities among words    . We design a new comprehensive test set for
    measuring both syntactic and semantic regularities1 , and show that
    many such regularities can be learned with high accuracy. Moreover, we
    discuss how training time and accuracy depends on the dimensionality
    of the word vectors and on the amount of the training data.







    nlp language-model representation







    share|improve this question









    $endgroup$















      2












      2








      2





      $begingroup$


      Context: In the paper "Efficient Estimation of Word Representations in Vector Space" by T. Mikolov et al., the authors make use of the phrase: 'Linear regularities among words'.



      What does that mean in the context of the paper, or in a general context related to NLP?



      Quoting the paragraph from the paper:




      Somewhat surprisingly, it was found that similarity of word
      representations goes beyond simple syntactic regularities. Using a
      word offset technique where simple algebraic operations are performed
      on the word vectors, it was shown for example that vector(”King”) -
      vector(”Man”) + vector(”Woman”) results in a vector that is closest to
      the vector representation of the word Queen [20].



      In this paper, we try to maximize accuracy of these vector operations
      by developing new model architectures that preserve the linear
      regularities among words      . We design a new comprehensive test set for
      measuring both syntactic and semantic regularities1 , and show that
      many such regularities can be learned with high accuracy. Moreover, we
      discuss how training time and accuracy depends on the dimensionality
      of the word vectors and on the amount of the training data.







      nlp language-model representation







      share|improve this question









      $endgroup$




      Context: In the paper "Efficient Estimation of Word Representations in Vector Space" by T. Mikolov et al., the authors make use of the phrase: 'Linear regularities among words'.



      What does that mean in the context of the paper, or in a general context related to NLP?



      Quoting the paragraph from the paper:




      Somewhat surprisingly, it was found that similarity of word
      representations goes beyond simple syntactic regularities. Using a
      word offset technique where simple algebraic operations are performed
      on the word vectors, it was shown for example that vector(”King”) -
      vector(”Man”) + vector(”Woman”) results in a vector that is closest to
      the vector representation of the word Queen [20].



      In this paper, we try to maximize accuracy of these vector operations
      by developing new model architectures that preserve the linear
      regularities among words      . We design a new comprehensive test set for
      measuring both syntactic and semantic regularities1 , and show that
      many such regularities can be learned with high accuracy. Moreover, we
      discuss how training time and accuracy depends on the dimensionality
      of the word vectors and on the amount of the training data.







      nlp language-model representation




      nlp language-model representation






      share|improve this question













      share|improve this question











      share|improve this question




      share|improve this question










      asked 14 hours ago









      Dawny33♦Dawny33

      5,45183188




      5,45183188






















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          $begingroup$

          By linear regularities among words, he meant that "Vectorized form of words should follow linear additive properties!"



          V("King") - V("Man") + V("Woman") ~ V("Queen)






          share|improve this answer









          $endgroup$













            Your Answer





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            1 Answer
            1






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            active

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            5












            $begingroup$

            By linear regularities among words, he meant that "Vectorized form of words should follow linear additive properties!"



            V("King") - V("Man") + V("Woman") ~ V("Queen)






            share|improve this answer









            $endgroup$


















              5












              $begingroup$

              By linear regularities among words, he meant that "Vectorized form of words should follow linear additive properties!"



              V("King") - V("Man") + V("Woman") ~ V("Queen)






              share|improve this answer









              $endgroup$
















                5












                5








                5





                $begingroup$

                By linear regularities among words, he meant that "Vectorized form of words should follow linear additive properties!"



                V("King") - V("Man") + V("Woman") ~ V("Queen)






                share|improve this answer









                $endgroup$



                By linear regularities among words, he meant that "Vectorized form of words should follow linear additive properties!"



                V("King") - V("Man") + V("Woman") ~ V("Queen)







                share|improve this answer












                share|improve this answer



                share|improve this answer










                answered 13 hours ago









                PreetPreet

                2563




                2563






























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