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  1. Apr 2017
  2. levyomer.files.wordpress.com levyomer.files.wordpress.com
    dependency-based-word-embeddings-acl-2014.pdf
    4
    1. akcool123 19 Apr 2017
      arg maxvw;vcP(w;c)2Dlog11+evcvw

      maximise the log probability.

      dependency-word-embeddings-paper Skip-gram
    2. akcool123 19 Apr 2017
      p(D= 1jw;c)the probability that(w;c)came from the data, and byp(D= 0jw;c) =1p(D= 1jw;c)the probability that(w;c)didnot.

      probability of word,context present in text or not.

      dependency-word-embeddings-paper Skip-gram
    3. akcool123 19 Apr 2017
      Loosely speaking, we seek parameter values (thatis, vector representations for both words and con-texts) such that the dot productvwvcassociatedwith “good” word-context pairs is maximized.
      dependency-word-embeddings-paper Skip-gram
    4. akcool123 19 Apr 2017
      In the skip-gram model, each wordw2Wisassociated with a vectorvw2Rdand similarlyeach contextc2Cis represented as a vectorvc2Rd, whereWis the words vocabulary,Cis the contexts vocabulary, anddis the embed-ding dimensionality.

      Factors involved in the Skip gram model

      Skip-gram dependency-word-embeddings-paper NLP
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    levyomer.files.wordpress.com/2014/04/dependency-based-word-embeddings-acl-2014.pdf