Acquiring Strongly-related Events using Predicate-argument Co-occurring Statistics and Caseframe

Transcription

1 Web 96% 79.1% 2 Acquiring Strongly-related Events using Predicate-argument Co-occurring Statistics and Caseframe Tomohide Shibata 1 and Sadao Kurohashi 1 This paper proposes a method for automatically acquiring strongly-related events from a large corpus using predicate-argument co-occurring statistics and caseframe. The co-occurrence measure is calculated using an association rule mining method, and the importance of an argument for each predicateargument is judged. Then, the argument alignment in the pair of predicatearguments is performed by using a caseframe. We conducted experiments using a Web corpus consisting of 1.6G sentences. The accuracy for the extracted event pairs was 96%, and the accuracy of the argument alignment was 79.1%. The number of acquired event pairs was about 20 thousands. 1 Kyoto University 1. 1) ( ) 2) 3) P A 1 P A 2 A 1 :{,,...} A 1 :{,,...} A 2 :{,...} A 2 :{,...} A 3 :{ } A 1 A 2 P A 1 P A 2 A 3 P A 2 P A 2 Chambers 4),5) () (1) a. b. (1-a) P A 2 (1-b) P A 1 Chambers 2 P A 1 A 2 : {,...} P A 2 A 3 : { } 1 c 2011 Information Processing Society of Japan

2 A 2:{,...} A 3:{ } P A 1 A 2 :{,...} P A 2 P A 1 A 1 :{,,...} P A WordNet 6) WordNet LifeNet 7) 8 41 EventNet Openmind Commonsense Knowledge Base 8) Regneri Amazon Mechanical Turk 9) Lin 10) X is the author of Y X wrote Y X,Y Chambers 4),5) accused XX claimedx argueddismissed X / 12) - 13) 14) 3. 1 Web P A 1 P A 2 P A 1 P A 2 15) 1) c 2011 Information Processing Society of Japan

3 Web コーパス 述語項構造ペアの抽出 PA1 彼ガ財布ヲ拾う 財布ヲ拾う ドライバーガ財布ヲ拾う PA2 警察ニ届ける 警察ニ届ける 届ける 1 P A 1 P A 2 拾う : 10 ガ ヲ 男, 女の子, 財布, 電話, 格フレームに基づく項のアライメント PA1 財布ヲ拾う 警察ニ届ける A1 : { 人, 男, } ガ A2 : { 財布, } ヲ 1 述語項構造ペアの共起度計算 拾う PA2 届ける : 20 ガ ヲ ニ 男, 人, 財布, 金, 警察, 交番, A1 : { 人, 男, } ガ A2 : { 財布, } ヲ 届ける A3 : { 警察 } ニ P A 1 P A 2 P A 1 P A 2 4. (2) a. b. 2 77,, 105,,, 502,, ID, 956,, 1829,, 1901,, 1 (P A 1 P A 2 ) P A 1 P A 2 P A 2 P A 1 ( P A 1 P A 2 ) P A 2 ( P A 2 ) 16) - 2,000 2 n (P (c n)) c 77 P A 1:, P A 2: P A 1: 3 c 2011 Information Processing Society of Japan

4 77, P A 2: 77 P A 1 :, P A 2 : P A 1 : 77, P A 2 : ) ) I = I 1, I 2,, I m t (t I) T (T = t 1, t 2,, t n ) X Y (X, Y I, X Y = φ) X Y X antecedent (left-hand side, lhs)y consequent (right-hand side, rhs) 3 support confidence lift support(x Y ) = C(X Y ) T confidence(x Y ) = C(X Y ) C(X) lift(x Y ) = confidence(x Y ) support(y ) = support(x Y ) support(x) C(X) X support XY confidence X Y lift X Y (1) (2) (3) 3 () P A 1 P A , , - Apriori 17) abc t 1 abcd t 2 t 1 t 2 Apriori support confidence 5.2 Apriori Apriori 4 3 X P A 1 P A 1 0 Y P A 2 P A 2 0 lift lift-min lift-max lift-max Apriori 3 ( 1 ) - - ( 2 ) - 4 c 2011 Information Processing Society of Japan

5 4 () :1 (2), (2), (3513), (80), :10 (4), (2), (580), (136), :1 (164), (144), (103400), (4797), :20 (11), (8), (8), (6), (2587), P A 1 - P A 2 - P A P A 1 P A 2 P A 1P A Web 1) 4 P A 1 cf 1 P A 2 cf 2 P A 1 P A 2 ( 1 ) P A 1 P A 2 5 (2) P A 2 ( 2 ) argmax cf 1,cf 2 max a sim(arg 1, a(arg 1)) (4) a a a P A 1 P A 2 arg 1 P A 1 a(arg 1 ) arg 1 P A 2 a arg1 a(arg 1 ) sim arg 1 a(arg 1) cosine :10:20 sim 2 cosine :10 ( 4, 2, 2, ) :20 ( 11, 8, 0, ) P A 1 P A 2 P A 1 10 P A 2 20 P A 1 P A 2,, c 2011 Information Processing Society of Japan

6 5 96(96.0%) 4(4.0%) 76(79.1%) 20(20.8%) 7 ( 6 ) P A 1 P A 2 6 (5 ) P A 1 P A 2 (1) - (2) - - (3) (4) (5) (6) (7) - (8) - - JUMAN 1 KNP Apriori support confidence lift-min, lift-max 1010, ) 30, (1) (2) A 1 :{,,...} A1 :{,,...} A 2 :{ } A 1 :{,,,...} A 1 :{,,,...} A 2 :{ } A 3 :{ } (3) A 1 :{,,,...} A 1 :{,,,...} A 1 :{,...} (4) A 1:{,...} A 2 :{,,...} A 2 :{,,...} (5) A 1 :{,,...} A 2 :{,,...} A 1 :{,,...} A 2 :{,,...} A 3 :{ } A 1 :{,,...} (6) A 2:{,,...} A 2 :{,,...} A 1 :{,,...} (7) A 1 :{ } A 1 :{ } 5 96% 6 ( 6 (8)) % 7 7 (6) P A 1 A 1 P A 2 A 1 A 1 A 3 A 1 :{,,...} A 2:{,,...} A 3 :{,,...} A 2:{,,...} A 3 :{,,...} 6 c 2011 Information Processing Society of Japan

7 7 (7) P A 2 P A 1 P A 1 P A 2 A 2 :{,,,...} A 2:{,,,...} A 1 :{ } 8 ( ) P A 1 P A (3,768 / 23,180) (549 / 1,944) (474 / 2,689) (753 / 2,764) (7,106 / 14,713) (1,054 / 3,284) (344 / 2,113) (1,042 / 3,086) (549 / 1,944) ) ( F ) ) Web 2 w v e(w, d)e(v, g) w d v g d g e(w, d) e(v, g) pmi(e(w, d), e(v, g)) = log P (e(w, d), e(v, g)) P (e(w, d))p (e(v, g)) k (k 5 ) 8 P A 1 P A 2 P A 1P A (5) 2 ( lift ) Chamber c 2011 Information Processing Society of Japan

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