( : ) [12] [1], [4], [14], [17], [19] Yoshinaga Torisawa [19] Takamura Tsujii [14] ( : ) [11] Kurashima [9] [2], [5], [10], [13], [16] (3. 2

Size: px

Start display at page:

Download "( : ) [12] [1], [4], [14], [17], [19] Yoshinaga Torisawa [19] Takamura Tsujii [14] ( : ) [11] Kurashima [9] [2], [5], [10], [13], [16] 2 3. 3. 1 (3. 2"

れれこしの
7 years ago
Views:

1 DEIM Forum 2016 P2-3,, {nari,ynaga,toyoda,kitsure}@tkl.iis.u-tokyo.ac.jp ( : ) ( : ) Twitter 1.,,, [20] () ( ) { } ρ 2 SVM (Ranking Support Vector Machine) [7] SVR (Support Vector Regression) [6] 8 3 Twitter 2. [20] PMI (Point-wise Mutual Information)

2 ( : ) [12] [1], [4], [14], [17], [19] Yoshinaga Torisawa [19] Takamura Tsujii [14] ( : ) [11] Kurashima [9] [2], [5], [10], [13], [16] (3. 2 ) ( : ) ( : ) ( : ) ( : ) 1 ( : ) SVM (Ranking Support Vector Machine) [7] SVR (Support Vector Regression) [6] SVM SVM () () ( ) PMI (1) PMI(x, y) = log p(x, y) p(x) p(y) 2 PMI Turney [15] 2 1 ϕ(x) ( x = (, )) 1 ( : ) (1)

3. 1 (3. 2 ) 3. 1 4 3 4 2 ( : ) ( : ) ( : ) ( : ) 1 ( : ) 1 3. 2 3.

3 1 SVM ( ) ( ) ( ) ϕ(x) ϕ(x) ϕ(x) / / (2) 2 ϕ(x) (2) = SO () = PMI(, [ or ]) PMI(, [ or ]) = log p(, ) p( ) p(, ) p( ) { } 1 2 ( 2 ) 1 ( ) SVM SVR SVM SVR SVR SVR n n! n min 2 ( ) ( ) SVR () ( 2 ) / N C 2 / N C 2 / N C 2 ( ) / N C 2 ( ) / N C 2 n min! ( ) () SO () > SO ( ) 1 4 ( ) 5 SVR SVM N Twitter Twitter Kaji [8] Yoshinaga [18] J.DepP 3 2 PMI

4 3 (a) ( ),,,,,,,,,, Ruby, Python, Perl, Java, JavaScript, Lisp, Scala, Haskell,,,,,,,,,, ( ),,,,,,, ROOKIES, GOEMON,,,,, ( ),,,,,, NEC,,,,,,,,,,, (b) ( ),,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,, Ruby, Python, Perl, Java, JavaScript, Lisp, Scala, Haskell,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,, ( ),,,,,,, ( ),, TOKIO,, SMAP,, ZARD, EXILE,, ONEPIECE,,,,,,,,,, ROOKIES, GOEMON ( ),,,,,,,,, NEC,,,, (),,,,,,,,,,,,,,,,,,,, Chrome, FireFox, Safari, Opera, Sleipnir,,,,,,,,,,,,, ( ),,,,,,,,,,,,, () Brown [3] 8 PMI ( ) 3

Haskell,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,, ( ),,,,,,, ( ),, TOKIO,, SMAP,, ZARD, EXILE,, ONEPIECE,,,,,,,,,, ROOKIES,

5 スピアマンの相関係数 ρ [ 花, 綺麗だ] [ 宝, 上品だ] [アルコール, 美味い] [スポーツ, 楽しい] [ 動物, 賢い] [ 乗り物, 快適だ] [ べ物, 美味い] [ 楽器, 地よい] [プログラミング語, 簡単だ] [ 動物, かわいい] [ 野菜, 美味しい] 1 3(a) [ 果物, い] [ 電化製品, 便利だ] [, 好きだ] [ 類, かわいい] [ 天候, 嫌いだ] [ 国, 安全だ] [お寺 ( 京都 ), 有名だ] (3 ) (3 ) (1 ) 7 ( 5 2 ) 7 ρ 3(b) 1 1 [] [] [] [] [] [] [] [ 歌 ( 本 ), 好きだ] [アニメ, い] [ 邦画, い] [ 芸能 ( 性 ), 可愛い] [ 電気メーカー, 有名だ] [ 野球チーム (メジャー), 有名だ] [ファストフード, 美味い] [ メーカー, 好調だ] [コンビニ, 便利だ] [ブラウザ, 快適だ] [ 都市名, 安全だ] 1 v b 2 v t 3 (1) (2) ( v b, v t) [カフェ, 好きだ] [ 地名 ( 東京 ), おしゃれだ] [ 出版社, 有名だ] leave-one-out ρ SVM SVR LIBLINEAR 4 SVM SVR C [20] 4

利だ] [, 好きだ] [ 類, かわいい] [ 天候, 嫌いだ] [ 国, 安全だ] [お寺 ( 京都 ), 有名だ] 20 30 (3 ) (3 ) (1 ) 7 ( 5 2 ) 7 ρ 3(b) 1 1 [] [] [] [] [] [] [] 4.

6 4 (a) [] Blog Tweet Blog + Tweet baseline svm svr baseline svm svr svm svr [] [ ] [ ] [] [] ( ) [ ] [ ] ( ) [ ] [ ] [ ] (b) ( human ) [] Blog Tweet Blog + Tweet human baseline svm svr baseline svm svr svm svr [] [] [] [] [ ] [] [] [] [] [] [] [ ] [] [] [] [] [] ( ) [] ( ) [] [] [] ( ) [] [] () [] [] [] [] [] [] [] ( ) [ ] [] SVM SVR

321 0.357 0.250-0.071 0.429 0.429 0.321 0.571 ( ) [ ] -0.179 0.321 0.179 0.143 0.143 0.143 0.071 0.321 [ ] 0.190 0.310 0.262 0.738 0.429 0.786 0.262 0.452 [ ] 0.300 0.300 0.300-0.100-0.100-0.600-0.

7 SVR [ ] SVM SVR SVR SVM SVR 4. 4 [] [ ] [] SVM SVR 1 [] SVM SVR [] 1 [] [] [ ] ( : ) ( : ) 1 [ ] [ ] 5. ( : ) ( : ) SVM SVR SVM SVR

8 1 () () ( : ) ( ) ( : ) ( : ) ( : ) JSPS [1] Sören Auer and Jens Lehmann. What Have Innsbruck and Leipzig in Common? Extracting Semantics from Wiki Content. In Proceedings of the 4th European Conference on The Semantic Web (ESWC), pp , [2] Ralph A. Bradley and Milton E. Terry. Rank analysis of incomplete block designs: I. the method of paired comparisons. Vol. 39, pp , December [3] Peter F. Brown, Peter V. desouza, Robert L. Mercer, Vincent J. Della Pietra, and Jenifer C. Lai. Class-Based n-gram Models of Natural Language. Computational Linguistics, Vol. 18, No. 4, pp , December [4] Hsin-Hsi Chen, Shih-Chung Tsai, and Jin-He Tsai. Mining tables from large scale HTML texts. In Proceedings of the 18th conference on Computational linguistics (COLING), pp , [5] Xi Chen, Paul N. Bennett, Kevyn Collins-Thompson, and Eric Horvitz. Pairwise ranking aggregation in a crowdsourced setting. In Proceedings of the Sixth ACM International Conference on Web Search and Data Mining (WSDM), pp , [6] Harris Drucker, Christopher J. C. Burges, Linda Kaufman, Alexander J. Smola, and Vladimir N. Vapnik. Support Vector Regression Machines. In Advances in Neural Information Processing Systems 9, NIPS 1996, pp MIT Press, [7] Thorsten Joachims. Optimizing Search Engines Using Clickthrough Data. In Proceedings of the Eighth ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD), pp , [8] Nobuhiro Kaji and Masaru Kitsuregawa. Efficient Word Lattice Generation for Joint Word Segmentation and POS Tagging in Japanese. In Proceedings of the Sixth International Joint Conference on Natural Language Processing, pp , Nagoya, Japan, October Asian Federation of Natural Language Processing. [9] Takeshi Kurashima, Katsuji Bessho, Hiroyuki Toda, Toshio Uchiyama, and Ryoji Kataoka. Ranking Entities Using Comparative Relations. In Proceedings of the 19th Conference on Database and Expert Systems Applications (DEXA), pp , [10] Shuzi Niu, Yanyan Lan, Jiafeng Guo, and Xueqi Cheng. Stochastic rank aggregation. In Proceedings of the 29th Conference on Uncertainty in Artificial Intelligence (UAI), pp , [11] Bo Pang and Lillian Lee. Opinion Mining and Sentiment Analysis. Now Publishers Inc., [12] John Prager. Open-Domain Question Answering. Now Publishers Inc., [13] Karthik Raman and Thorsten Joachims. Methods for ordinal peer grading. In Proceedings of the 20th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD), pp , [14] Hiroya Takamura and Jun ichi Tsujii. Estimating Numerical Attributes by Bringing Together Fragmentary Clues. In Proceedings of the 2015 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, pp , [15] Peter Turney. Thumbs Up or Thumbs Down?: Semantic Orientation Applied to Unsupervised Classification of Reviews. In Proceedings of the 40th Annual Meeting on Association for Computational Linguistics, pp , [16] Maksims N. Volkovs and Richard S. Zemel. A flexible generative model for preference aggregation. In Proceedings of the 21st International Conference on World Wide Web (WWW), pp , [17] Fei Wu and Daniel S. Weld. Autonomously semantifying Wikipedia. In Proceedings of the sixteenth ACM conference on Conference on information and knowledge management (CIKM), pp , [18] Naoki Yoshinaga and Masaru Kitsuregawa. Kernel Slicing: Scalable Online Training with Conjunctive Features. In COLING, pp Tsinghua University Press, [19] Naoki Yoshinaga and Kentaro Torisawa. Open-domain attribute-value acquisition from semi-structured texts. In Proceedings of the 6th International Semantic Web Conference (ISWC-07), Workshop on Text to Knowledge: The Lexicon/Ontology Interface (OntoLex-2007), pp , [20],,,.. IPSJ SIG NL, Vol. 2013, No. 8, pp. 1 7, nov 2013.

the method of paired comparisons. Vol. 39, pp. 324 345, December 1952. [3] Peter F. Brown, Peter V. desouza, Robert L. Mercer, Vincent J. Della Pietra, and Jenifer C. Lai.

( : A8TB2163)

2011 2012 3 26 ( : A8TB2163) ( A B [1] A B A B B i 1 1 2 3 2.1... 3 2.1.1... 3 2.1.2... 4 2.2... 5 3 7 3.1... 7 3.2... 7 3.3 A B... 7 4 8 4.1... 8 4.1.1... 9 4.1.2... 9 4.1.3... 9 4.1.4... 10 4.2 A B...