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構文や語彙意味論の分析成果をプログラムとして具現化する言語 パターンマッチAPIの可能性

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構文や語彙意味論の分析成果をプログラムとして具現化する言語 パターンマッチAPIの可能性の発表スライドver1

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構文や語彙意味論の分析成果をプログラムとして具現化する言語 パターンマッチAPIの可能性

  1. 1. API 2 ( 239 ) 2019 3 3
  2. 2. n n 1998 (2 ) n è ( )è n n HMM n n (HMM ( )) n ( ) n ( ( NICT)) n (NLC ) n ( ) 1
  3. 3. nend-to-end 2 ( ) ( ) è ( )API end-to-end
  4. 4. : 1 3 : ( ) end-to-end : IF ( ) ç 2 (e.g. QA) (e.g. )A B è B ( ) e.g. / / ( ) e.g. ( ) pizza(X) ::= { | } X crust(Z) ::= Z : NLC2015 2 +2016 (IPA, UniDic, Neologd..) è è take(A,T)::= T { | | } yearOld(A) < 20? yearOld(A) > 40? A: 2015 (A,T)::= T A | (A,T)| (A,T)| (A,T) (A,T) ( ) IBM Watson (prolog), DIAL Feldman+(2007) The Text Mining Handbook (Feldman2007) OpenCV R
  5. 5. n end-to-end n or ( ) n and n end-to-end n n end ( ) ( )ç n e.g. FAQ ( ) n e.g. † n n ( ) n end-to-end / ? n e.g. n cf. Neologd † n e.g. You’ve lost me kick the long bucket 4† 2016 2017 +2007 (2007) 13 pp. 396-399, NLP2007
  6. 6. n 5 e.g. ChatBot 2 (e.g. QA) (e.g. )A B A ( ) 1 1 IF ( ) ( ) ç ( ) ç
  7. 7. : n (QA) 6 e.g. ? è 3 ? A Aggregation classifier TB IF SQL count select Id Pi Cr N ... 1 2 3 4 from TB where crust = SQL 3 3 (Domain Specific Language) (Seq2SQL ) . ? Select column pointer WHERE clause pointer decoder Zhong+ 2017, Seq2SQL : Generating Structured Queries from Natural Language using Reinforcement Learning https://einstein.ai/static/images/pages/research/seq2sql/seq2sql.pdf : ( = ) => => select count from where =
  8. 8. : n ( ) 7 e.g. x (set) : (QA ) : B = (DSL ) ! (take(A,x) ) ( x ) A={a | x (a)} Y={y | A } take(A,T)::= T { | | }Y{y|take(A,x) & yearOld(A)} {20, 25, 23, 45, 56, 35, ...} ( A ) x x ( ) † (2006) /† (2015) NLC
  9. 9. : DIAL: 8 : : ( ) concept Person{ attributes: Title, FirstName, MiddleName, LastName } rule Person{ pattern: “mr.” -> title Capital-> first Capital->last actions: Add (Title<- title, FirstName<- first, LastName<-last) } rule Person{ pattern: Captial->first MiddleNameConcept->mid Capital->last actions: Add(FirstName<- first,MiddleName<-mid, LastName<-last) } mr. 2 first, last 1 2 2 first, last ) Feldman+09
  10. 10. : prolog 9 :QA (IBM Watson (Jeopady!) ) è lemma(1, ‘‘he’’). partOfSpeech(1,pronoun). lemma(2, ‘‘publish’’). partOfSpeech(2,verb). lemma(3,‘‘Songs of a Sourdough’’). partOfSpeech(3,noun). subject(2,1). object(2,3). he publish Songs of a Sourdough 1 2 3 [pro] [verb] [noun] Prolog A. Lally+ 2012. Question analysis: How Watson reads a clue, IBM Research & Development Vol. 56, No. ¾, Paper 2. authorOf(Author, Composition) :- createVerb(Verb), subject(Verb, Author), author(Author), object(Verb, Composition), composition(Composition). createVerb(Verb) :- partOfSpeech(Verb, verb), lemma(Verb, VerbLemma), [‘‘write’’, ‘‘publish’’, . . .]. • authorOf • createVerb • WordNet Prolog , and
  11. 11. : 10 NTT ( ) : ? https://nttdata-nazuki.jp/index.html (2014) Twitter NLC 2014 9 11 (1) ( +NTT ) ( 2014) è A BB C : : twitter C : + twitter (2) (https://markezine.jp/article/detail/26132?p=3) è ( ) ( )
  12. 12. : 11 : (concordancer) http://chasen.naist.jp/chaki/t/2009-09-30/doc/chaki-nlp-seminar090930.pdf ChaKi.NET ( 2009)Sketch Engine:
  13. 13. 12 n end-to-end n n n IF è ( ) è n SQL, ó è ? è IF IF , ChatBot QA
  14. 14. 13 n n OpenCV R API n API n n : (A,T)::= {T A | A T { | } | { (A,T) | #1(A,T) | (A,T)}}. ( # ) ( ) (2003,2011) (2011) Meyers+2004
  15. 15. 14 (A,T)::= {T A | A T { | } | { (A,T) | #1(A,T) | (A,T)}}. (Generative Lexicon ) è Formal (X) (X) (X) Constitutive Telic ( , =X) Agentive ( Z, X) ( X Y) ( X, Y) + (2015) ( WS) (2011), + (2015) (Constitutive role): (Formal role): (Telic role): (Agentive role): (Pustejovsky 1995) X Y /Y X
  16. 16. : 15 : , , (A,T)::= {T A | A T { | } | { (A,T) | #1(A,T) | (A,T)}}. 1 2 3 4 5 6 7 A T Span è Span1 Span2 Agent Theme A T ( , ) è Span (IF ) (1,2)
  17. 17. API 16 9:00 16:00 15 1 2 ? ? ) QA ( ) YobiFromTo(X1, X2):- {X1 X2} Yobi(X1), Yobi(X2). Yobi(Z):- [ , , ]{null| } TimeFromTo(T1, T2):- TimePattern(T1), TimePattern(T2), {T1 T2|T1 T2}. TimePattern(T1) :- {T1 = HHMM| HH:MM} ( 9:00 16:00 ) è IF (API) API YobiFromTo( , ) TimeFromTo(9:00, 16:00) Date Time API
  18. 18. : 17 ( + 2018) , , , (2018) JSAI 2018 2E2-02 : E-2-1 - - (V= , x) ::= { x | x | x | x } - (V= , x) ::= { x | x | x | x } :
  19. 19. : n 18 ReasonP (X) :- {X “ ” | X “ ” | X “ ” | X “ ”} Filered(c = “ ”,X) :- ReasonP(X), Filter(c, X). 3 , , . . , Vol. 45, No. 3, pp. 919–933, 2004 , , , (2008). . NLP2014, 1144-1147
  20. 20. : API 19 Ju+KN Uni-M+Cab Kuro Sudachi FrameNet (A,T) n n (A,T) n n n python Contain(A, “ ”) And Get(T) Get(T):- Author(A,T), Contain(A,” ”) ) API T { , , , }
  21. 21. (2019 2 ) n FaceBook PyText ( ) n Semantic Parsing for Task Oriented Dialog using Hierarchical Representations 20 (Intent slot) Gupta+ (2018) Semantic Parsing for Task Oriented Dialog using Hierarchical Representations, EMNLP2018, pp.2787-2792, http://aclweb.org/anthology/D18-1300/d18-1300 Get_Directions (Destination) ::= { “Driving directions to ” } Destination (Name_event, Cat_event) ::= {event } PyTorch
  22. 22. : 21 n API Aggregation classifier TB IF SQL count select Id Pi Cr N ... 1 2 3 4 from TB where crust = 3 3 (Seq2SQL ) . ? Select column pointer WHERE clause pointer decoder or NN IF SQL count select Id Pi Cr N ... 1 2 3 4 from TB where crust = 3 NN 3 API( ) Neura lNetwork backprop API softmax
  23. 23. 22 n n Strubell+(2018) end-to-end parser è Dozat & Manning (2017) parser Strubell, E., Verga, P., Andor, D., Weiss, D. and Mc- Callum, A. (2018) Linguistically-Informed Self-Attention for Semantic Role Labeling . Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing, pp. 5027–5038 Dozat, T. and Manning, C. D.: Deep Biaffine Atten- tion for Neural Dependency Parsing, Proceedings of the International Conference on Learning Representations (2017). End-to-End
  24. 24. 23 ( ) Self-Supervised Learning cbow skip-gram BERT : • NHK ? AI 2 1 • (2019). NeurIPS 2019 . in 76 AI AI 1 2019 3 1 • (2019) in (2018) . (google ) : Q ? ? ( )
  25. 25. n n ( ) n n n n èAPI n n OpenCV R n API n ( ) n n ( ?) 24
  26. 26. Reference n : NLC2015 2 n (2016) , NLC2015-46 n Feldman+ (2007) The Text Mining Hnadbook ( : + (2010) ) n (2011) . n (2007) . n (2003) . n A. Meyers and R. Reeves and C. Macleod and R. Szekely and V. Zielinska and B. Young and R. Grishman (2004) “AnnotaVng Noun Argument Structure for NomBank,” in Proceedings of LREC2004, pp. 803–806. n , (1987) . n (2013) n , , 6 , pp.51-56, 2014 . n , : No.2, pp.85-120, 2006. n , , 10 , A5-3, 2004 n Liang+2017, Neural Symbolic Machines: Learning SemanVc Parsers on Freebase with Weak Supervision, ACL2017 (SQL s2s). 25
  27. 27. n 26

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