ways to represent document by its keyword vectors

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ways to represent document by its keyword vectors














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$begingroup$


I have documents, say for example, D1, D2, D3... Dm.



Every Di has its individual components or keywords k1, k2, k3,... kn, where ki is an n-dimensional vector. The number of individual components varies between documents.



What are the ways to find how close Di are? Or what is the best way to represent a document using its keyword vectors? Please note that I'm using a custom embedding here.






nlp word-embeddings similar-documents vector-space-models






share









$endgroup$
















    0












    $begingroup$


    I have documents, say for example, D1, D2, D3... Dm.



    Every Di has its individual components or keywords k1, k2, k3,... kn, where ki is an n-dimensional vector. The number of individual components varies between documents.



    What are the ways to find how close Di are? Or what is the best way to represent a document using its keyword vectors? Please note that I'm using a custom embedding here.






    nlp word-embeddings similar-documents vector-space-models






    share









    $endgroup$














      0












      0








      0





      $begingroup$


      I have documents, say for example, D1, D2, D3... Dm.



      Every Di has its individual components or keywords k1, k2, k3,... kn, where ki is an n-dimensional vector. The number of individual components varies between documents.



      What are the ways to find how close Di are? Or what is the best way to represent a document using its keyword vectors? Please note that I'm using a custom embedding here.






      nlp word-embeddings similar-documents vector-space-models






      share









      $endgroup$




      I have documents, say for example, D1, D2, D3... Dm.



      Every Di has its individual components or keywords k1, k2, k3,... kn, where ki is an n-dimensional vector. The number of individual components varies between documents.



      What are the ways to find how close Di are? Or what is the best way to represent a document using its keyword vectors? Please note that I'm using a custom embedding here.






      nlp word-embeddings similar-documents vector-space-models




      nlp word-embeddings similar-documents vector-space-models





      share












      share










      share



      share










      asked 1 min ago









      Van PeerVan Peer

      103117




      103117




















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