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1 SP-G 2011 AI An Automatic Question Generation Method for a Local Councilor Search System Yasutomo KIMURA Hideyuki SHIBUKI Keiichi TAKAMARU Hokuto Ototake Tetsuro KOBAYASHI Tatsunori MORI Otaru University of Commerce Yokohama National University Utsunomiya Kyowa University Fukuoka University National Institute of Informatics Yokohama National University keywords: local politics, question generation, information extraction Summary This paper presents an automatic question generation method for a local councilor search system. Our purpose is to provide residents with information about local council activities in an easy-to-understand manner. Our designed system creates a decision tree with leaves that correspond to local councilors in order to clarify the differences in the activities of local councilors using local council minutes as the source. Moreover, our system generates questions for selecting the next branch at each condition in the decision tree. We confirmed experimentally that these questions are appropriate for the selection of branches in the decision tree. 1. TV 1 22 A4 200

2 581 1 n [ 08, 09, 09a, Takamaru 09, 09, 09b, 10, 10] ( ) SVM SVM [ 10]

3 SP-G 2011 [ 09] [ 08] , , [ 09] XML <Paragraph> % Web , % % % % % , % % % % % , % , % , % , % % % % % , % % % % % % <Keyword> <Keyword> Member Category <Paragraph> Member

4 583 2 <Paragraph Member= 37 > <Keyword Member= Category= 4110 > </Keyword> <Keyword Member= Category= 4110 > </Keyword> <Keyword Member= Category= 1050 > </Keyword> <Keyword Member= Category= 1050 > </Keyword> </Paragraph> <Paragraph Member= > </Paragraph> <Paragraph Member= > </Paragraph> <Paragraph Member= > <Keyword Member= Category= 1010;1101 > </Keyword> <Keyword Member= Category= 1101 > </Keyword> <Keyword Member= Category= 1030 > </Keyword> <Keyword Member= Category= 1030 > </Keyword> </Paragraph> <Paragraph Member= > </Paragraph> ID3 C4.5 C4.5 Weka J48 4 [ 03] 3 1 Weka J48 4

5 SP-G A B C n P1 P2 P1 P2 M1 P2 P1 M2 n n ,140 18, C 3 =1, C 3 =18,

6 585 2 (1) (2) A ID=1 A C A C A B B ID=2 B ID=5 ID=5 A B

7 SP-G A B A B [ 09] 6 A B A B A B A B A B 2 [ 99] [ 09] A B A B - A B A B A B A B 4 2 A B 19 - A B 19 A B 14,336 5 A B A B - - A B IPADIC IPADIC - IPADIC A ( / / ) B A B [ 00] A BA B A B A B GoogleN-gram [Google 07] Google N-gram ,

8 587 A B A B A A B ,667 Google N-gram A B A B CaboCha [Kudo 03] CaboCha A 7 7 A B A A A B A B 7 A B A B %=(71/90) 1 A B A B ( ) A B A B 7 #

9 SP-G Google N-gram A B Google N-gram A B ( ) IDF (1) Baseline() - A B A B Google N-gram A B Baseline (2) A B A B (3) 3 (4) IDF(Inverse Document Frequency) ICF(Inverse Category Frequency) ICF IDF Document Category Document CF t ICF t

10 # # # # # # # # A B N ICF(w i )=1+log 2 CF(w i ) w i N 19 7, CF( ) ICF ICF ICF ICF = 1 n n ICF(w i ) i=1 ICF ICF( ) ICF + L (L) ICF ICF ICF + L = 1 n n ICF(w i ) logl(w i ) i=1 L(w i ) w i ICF ICF( )+L A B

11 SP-G = 4 3 = (=17/20) ICF Mecab 1 IPA : 2 1 : 3 : : : 0.95(=19/20)

12 591 9 / / Baseline() 7/ / ( ) 15/ / ( ) 5/ / ( ) 16/ / / / ICF 3/ / ICF( ) 4/ / ICF + L 6/ / ICF( )+L 5/ / / / 17/ / / / / / / / : 19/ / :+ 19/ / / / Baseline() 8/ / ( ) 21/ / ( ) 20/ / ( + ) 22/ / / / / / / / / / : 23/ / :+ 22/ / : + : (=25/37)

13 SP-G twitter( ) [ 03],,, Boosting, 4 (2003) [Google 07] Google Web N 1 by Google, GSK GSK2007-C (2007) [ 08],,,,,, 2008-NL-187, pp (2008) [ 00],, N1 N2,, Vol. 7, No. 4, pp (2000) [ 09a],, 2009 (2009) [ 09b],,,,25 1, pp (2009) [ 10],,,, 16, pp (2010) [Kudo 03] Kudo, T. and Matsumoto, Y.: Fast Methods for Kernel- Based Text Analysis, ACL 2003 (2003) [ 99], A B,, Vol. 129, No. 16, pp (1999) [ 09],,,,, 15, pp (2009) [ 10],,,,, NLC2010-1, Vol. 110, No. 142, pp (2010) [ 09],,,,, Vol. 109, No. 234, pp (2009) [Takamaru 09] Takamaru, K., Shibuki, H., Kimura, Y., Hasegawa, D., Ototake, H., and Araki, K.: Extraction of Political Activity of Assemblyman from Minutes of Municipal Assemblies Using the Political Category, Proc. 11th Conference of Pacific Association for Computational Linguistics (PACLING 2009), p. B11 (2009) [ 09], - -,, Vol. 25, No. 1, pp (2009)

14 ( ) New York 2002 ( ) 2006 ( ) , Rutgers, Stanford Stanford CSLI ACM

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