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Event Relation Extraction

Event Relation Extraction

所要で事態間知識の抽出についてサーベイする機会があったので、そのまとめです。
→ Common Knowledge reasoning, Event rcausality relation inference 周辺だと文献調査で判明したので、別途またまとめます。(2019-03-20追記)

izuna385

March 07, 2019
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  1. 2/10  vs  • RSebe7K _ be;?Z D(Chambers, 2008

    ACL) RSeZJd`^/6e>cKC \]9Y Bp (FX, NLP2015) 5%'1(2,MWShibata(2011,IJCNLP) 3 fQ7Lm;jI PMI"PU :@ n: 7L.#o! (Higashiyama, 2016) 8: Shibata,2011 IJCNLP iH"→O hE→G :@ tke.# rfQ<keqVl A! s Apriori Algorithm t T-02/(ke *$%,4e&),[! +2'&),) 340,000.#7Lmag"N= 2
  2. 3/10 !#+ -', ) • (NLP2011)  LifeNet data 

    (  Temporal("-.) & / 0 *-. / %.$
  3. 4/10 BDT ( • %(NLP, 2008) ]7A&=Y85 "#@H →,1 Z,&=

    I+V+*> ^[→-6  ,1 4FZ, 9P 2Q< • `J(HLT-NAACL, 2006) .C 3SKL &=YZ,?/ →0OWT \aNb!$#_→ aNbX • E:(';MQ9), 2016) RUG
  4. 5/10 DEY Higashiyama 2017 • F8(+9QU6,, 2016) \2^(IEEE,2017) PMI: )?%0Z

    WY$#('→WY$#('0Z )?%  1;O7S  PMI )?7S0ZRM[B7S  2016<Web!($"2017IDCTCorpus-N /4 VGCabocha X.20173A:)?%L=PO+@  &*HK>] NJ20165 
  5. 6/10   or   • Kayesh(arxiv,2019) Preprocess +

        WordVec emb layer feature extraction NN Train+test 500  
  6. 7/10 I3 (Deep$M) • #7KAF 0C  given /J9; →

    2F(Common Sense)?5P given  >*189;NFAQO →  " H ?5P '( L% :K@NTemporal relation, )4L%O -<N+- B< / =L% / &H #GO  • Event Detection + Relation Classification D ., Relation Classification !6E
  7. 8/10 " (Deep$) • ! Improving Event Causality Recognition with

    Multiple Background Knowledge Sources Using Multi- Column Convolutional Neural Networks (AAAI2017) • Embedding +   #→ Relation Classification  % !&
  8. 10/10   Komachi( ) Support Vector Machines  

       (     "!)       SVM