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1 DEIM Forum 2015 A ( ) ,,,, A Complementary Framework for Collecting Know-How Knowledge based on Question-Answer Examples and Search Engine Suggests Ichiro MORIYA, Takakazu IMADA, Yusuke INOUE,TianNIE,TakehitoUTSURO, Yasuhide KAWADA, and Noriko KANDO Grad. Sch. of Systems and Information Engineering, University of Tsukuba, Tsukuba Japan Faculty of Engineering, Information and Systems, University of Tsukuba, Tsukuba Japan Logworks Co., Ltd. Tokyo , Japan National Institute of Informatics, Tokyo , Japan 1. Wikipedia 1 Yahoo!

2 % %

3 1 LDA K : (LDA; Latent Dirichlet Allocation) [1] LDA w V w(w V ) K z n (n =1,...,K) w P (w z n)(w V ) b z n P (z n b) (n =1,...,K) GibbsLDA++ 3 LDA α β GibbsLDA++ α =50/K β =0.1 LDA K Gibbs 2,000 K 1 w V Wikipedia 4 GibbsLDA++ z n P (w z n) w N N = Wikipedia ,000 D K 1 d (d D) z n (n =1,...,K) D(z n) D(z n)= Ò d D z n = argmax z u (u=1,...,k) Ó P (z u d) d d 2. 3 d 20 6 z n P (w z n) w LDA [6]

4 Yahoo! ( : 16,257,413 : 50,053,894 ) d q D q D q = {d 1 q,...,d k q } 357,760 50,000 LDA D q LDA AND Google , Ë s Ë s AND N p 6 2 AND AND 7

5 5 : (a) (b) ,059 11,144 25,203 35,426 14,409 49,835 È(s,N) ( N =20 ) D w D w = È(s, N) s Ë Yahoo! Search BOSS API AND p p È(s,N) s Ë(p) Ò Ë(p) = s Ë Ó p È(s,N) D w LDA 4 zn w D(zn w ) Ë(zn) w Ë(zn)= w Ë(p) p D(zn w) Ë(z w n) D q D w D qw D qw = D q Dw D qw LDA P (w z n) w 5(a) 9 [6]

6 図 3 質問回答サイトのノウハウ収集 集約およびウェブからの新ノウハウ補足の例 (検索対象: 花粉症 ) 図 4 質問回答サイトのノウハウ収集 集約およびウェブからの新ノウハウ補足の例 (検索対象: 結婚 ) 研究 をノウハウ以外の知識 病院の診察時のトラブル を意 た 収集された話題の中には 花粉症の温熱治療のための吸入 見 花粉症の広告 をその他に分類した 検索対象 結婚 に 器 のように ウェブページのみから得られるノウハウ知識が おいては 芸能人の結婚 等をノウハウ以外の知識 結婚相手 合計で 19 個あり 全話題の約 35%となった 一方で質問回答 の外見についての相談 等を意見 結婚占い をその他に分類 サイトのみから得られるノウハウ知識は合計で 6 個あり 全話 した 結婚 に関するノウハウ知識を収 題の約 11%となった 一方 5. 3 ノウハウ知識収集結果の分析 集した結果においては 合計 35 個の話題が収集された 収集 情報源ごとのノウハウ知識の分析 された話題の中には 結婚生活での夫婦円満の秘訣 のように 表 5(a) に示すように 検索対象 花粉症 に関するノウハウ ウェブページからのみ得られるノウハウ知識が合計で 7 個であ 知識を収集した結果においては 合計 55 個の話題が収集され り 全話題の 20% となった 一方で質問回答サイトのみから

7 6 (%) 10 Yahoo! Yahoo! 5.0 (25/500) 8.4 (42/500) 8.5 (946/11,144) 16.6 (1,847/11,144) 5.6 (28/500) 16.8 (84/500) 7.0 (1,007/14,409) 22.1 (3,179/14,409) 12 34% Yahoo! D w 6 10 Yahoo! 11 6 Yahoo! 8.5% LDA (chienowa-qa.com) Yahoo! (chiebukuro.yahoo) (komachi.yomiuri) (oshiete1.nifty)!goo(oshiete.goo) (q.hatena) (qa.excite)!q&a(qanda.rakuten) Sooda!(sooda.jp) BIGLOBE (soudan1.biglobe) URL (b) [7] NTCIR Task Mining Task NTCIR-11 Task Mining Task [15] [11] Task Mining Task [10] Task Mining Task 12

8 [14] [3] [9] [2,4,8,12] [5] [13] 7. Yahoo! LDA % % [1] D. M. Blei, A. Y. Ng, and M. I. Jordan. Latent Dirichlet allocation. Journal of Machine Learning Research, Vol.3, pp , [2],,,,,.. 29, [3],,,. Web., NLC , pp , [4],,,,,,.. 28, [5],,,,.. 29, [6],,,,,.. 21, pp , [7],,,,. web. 6 DEIM, [8],,,,,,,.. 20, pp , [9],,.., Vol. J95-D, No. 3, pp , [10] Y. Liu, R. Song, M. Zhang, Z. Dou, T. Yamamoto, M. Kato, H. Ohshima, and K. Zhou. Overview of the NTCIR-11 IMine task. In Proc. 11th NTCIR Workshop Meeting, pp. 8 23, [11] S. Mine, T. Matsumoto, T. Yoshida, T. Shinohara, and D. Kitayama. InteractiveMediaMINE at the NTCIR-11 IMine search task. In Proc. 11th NTCIR Workshop Meeting, pp , [12],,,,,. Wikipedia. 6 DEIM, [13],,,,,,,.. 21, pp , [14],,. Web QA. WebDB Forum 2010, [15] T. Yumoto. University of Hyogo at NTCIR-11 TaskMine by dependency parsing. In Proc. 11th NTCIR Workshop Meeting, pp , 2014.

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