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文献紹介 11月7日

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October 31, 2018
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文献紹介 11月7日

Explicit Retrofitting of Distributional Word Vectors

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gumigumi7

October 31, 2018
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  1. Goran Glavas, Ivan Vulic Proceedings of the 56th Annual Meeting

    of the Association for Computational Linguistics (Long Papers), pages 34–45     
  2. .& 3 n *4 #/NLP ,60    n

    *4 #12,)-5*4+7*4 8%!"$  -34('   9 
  3. 5' 4 n Word vector space specialization l !$3LHGV=4 *JI:+

    3L!$M9C l $%$@? 1PB(3L8O  n Retrofitting l 3LD!$4 0TSE R>N9C l 0TSE2.!$@? ,QUV&6 n 2 9CK- explicit retrofitting (ER) ;A l 7)F!$/<"%#$  3L
  4. "$ 6 n 3)+-* !%& 1/+-  l Ex) car

    – automobile - car – drive - n Negative Sampling   l  '0 #-K+,2  (.
  5. ") 7 n .!"  #" 2,.  !# #"

    ′ %1&%(#" ; ()- l (:  0 n /' 2 +*1&$(
  6.  8 n 2"#%$ Mean Square Distance Objective (ER-MSD) n

    !": #% (&!" 0 ) n !($% , $' ): #%$ ( &,   #% )
  7.  9 n )$%$% '! ' Contrastive Objective (ER-CNT) n

    !":  &( ()$%  0 ") n !′: &( ' !(%& , %( ) # n ER-MSD )$%$% 
  8. '+ 10 n  .1 %#&",2$  *!3 Topological Regularization

    n !: ER-MSD  ER-CNT -,2( n ": *0! *!  )/, -,2( !# = ! + "!&'(
  9. 3G 11 n &+2> $ Word2Vec, GloVe, FastText ': n

    F=@4=@5( WordNet, Roget’s Thesaurus ': l 1,023,082F=@ 380,8734=@5( l !%.@7 57,320@1 → Retrofitting /E  )<.@06 ,8;?  n ACB7!(#$ , #& )!%#AC': 9*-D" %(0.3:
  10.  12 n      n 

    l Word Similarity (SimLex-999, SimVerb-3500) l Language Transfer n  l Lexical Text Simplification (LIGHT-LS) l Dialog State Tracking
  11. 7- (Word Similarity) 13 • SimLex 4 %#< !6>'&$ +.095(;=

    • :23  Attract-Repel  !6&$,"  8*&$)/ 1  ,"  
  12. /* (Lexical Text Simplification) 14 • Retrofitting (,  Attract-Repel

    % 2'135 • #. 59.6%  Attract-Repel %"!'   +$'1 7  A: Accuracy (-&), C: Changes ( 640))
  13. -* (Lexical Text Simplification) 15 • 1+ $."!2 3 )

    %04&    (,villain(%#) Attract-Repel protagonist(#)/' • ER-CNT demon(5)/'
  14.   16 n Retrofitting Word vector space specialization =*

    3 #"%$4916 n WordNet (<.+ ,*  :/-2 8)0>'!#7; n Word Similarity  Lexical Simplification A& 5.!#?0>@B