指定指示・代行指示を区別した指示連体詞の照応解析

∗ † ‡ † †† {naoya-i,inui,matsu}@is.naist.jp‡ ryu-i@cl.cs.titech.ac.jp1 (1)(1) (1) () (2) (2) 5 [7, 5] 2 1 ∗ Resolving direct and indirect anaphora for Japanese demonstrative determinersNaoya Inoue † , Ryu Iida ‡ , Kentaro Inui † , and Yuji Matsumoto †† Nara Institute of Science and Technology‡ Tokyo Insititude of Technology 2 2 2 3 4 5 6 2 2.1 2 () () 2 1 1 2 2 1 2 [12] [12] 2.2 2.1 1 ()

(1) (4)C(2)A(5)D 1: (3)B: : ( 1-1) 2 () [12] A ( 1-2) B (1-3) C ( 1-4) D ( 1-5) 4 3 Message Understanding Conference(MUC) *1 Automatic Content Extraction(ACE) *2 [7, 5]MUC-6 F 7 Bridging Reference [1] Poesio [6] Vieira [9] *1 http://www-nlpir.nist.gov/related projects/muc/index.html*2 http://www.nist.gov/speech/tests/ace/ (i) (ii) (iii) 3 2.2 [11] 100 2.2 A 4 NAIST [13] 2,929 (38,384 ) NAIST () *3 ACE 1,449 ( 869 580) 4,087 31.0%(1,269/4,087) 57.2%(2,336/4,087) 11.8%(482/4,087) 5 869 1,754 10 2 Support Vector Machine(SVM)[8] SVM SVM light*4 c=1.0 1,363 ( 40.6%(553/1,363)59.4%(810/1,363) ) 0.6%(11/1,754) 14.7%(258/1,754) 8.2%(122/1,485) *3 identity-of-sense anaphora [4]*4 http://svmlight.joachims.org/

1: CANDIDATES SENTDISTCAND i CAND ANP SENTDISTCAND i ANP CAND ANP COMB {HEAD, CASE, POS NE} CAND i ANP CAND DEPENDEDCAND i CAND {POS***, NE*, CASE}CAND i CAND PARRARELCAND i 1 0CAND DESCRIPTIVECAND i 1 0(a)CAND EOS ANP BOSCAND ANP NOUN SIM*CAND i ANP 1 0CAND i ANP CAND ANP HYPONYM OF ANP*CAND i ANP 1 0CAND ANP SYNONYM OF ANP*CAND i ANP 1 0CAND ANP STR LAST MATCH( HEAD)* CAND i () ANP 1 0CAND ANP STR PART MATCH( HEAD)* CAND i () ANP 1 0CAND ANP STR COMP MATCH*CAND i ANP 1 0CAND ANP PMI**CAND i ANP ANP TYPE***ANP {POS***, CASE}ANP ANP HEADANP MAX PMICAND i ANP (b)MAX NOUN SIMHAS SYNONYM OF ANPCAND i ANP ANP CAND i 1 0HAS HYPONYM OF ANPANP CAND i 1 0HAS STR LAST (HEAD) MATCHED ANP() CAND i 1 0HAS STR COMP MATCHEDANP CAND i 1 0(a) CAND i 2 ANP ‘*‘‘**‘(b) a ‘***‘CAND i C 2 5.1 1 (3) [10] [3] (3) ... ... 20 pLSI [2] ( 1,000) (4) ( A) ( B) 20 (4) ... A B ...5.2 2 = () α α(1) 2: 58.2%(322/553) 42.6%(345/810) 48.9%(667/1,363) 59.5%(329/553) 28.8%(233/810) 41.2%(562/1,363) A,B,C 2 3 2 1 2 • 1 2 • 2 2 3 A C

0.50.450.40.35PREUBLATBLATALATC0.3-1 -0.5 0 0.5 1PRE LAT {A,B,C} A, B, C UB 2: • B • B A 5.3 10 SVM 5 50 (26/50) (5) (5) ... ...21 4 9 1 6 0.90.850.8LATBLATCLATAPRE-1 -0.5 0 0.5 1 3: 3 [1] Clark, H. H.: Bridging, Thinking: Readings in Cognitive Science(1977).[2] Hofmann, T.: Probabilistic Latent Semantic Indexing, Proceedingsof the Twenty Second Annual International SIGIR Conference on Researchand Development in Information Retrieval, pp. 50–57 (1999).[3] Isahara, H., Bond, F., Uchimoto, K., Utiyama, M. and Kanzaki,K.: Development of the Japanese WordNet, In Proceedings of theSixth International Language Resources and Evaluation (LREC’08)(2008).[4] Mitkov, R.: Anaphora Resolution, Studies in Language and Linguistics,Pearson Education (2002).[5] Ng, V. and Cardie, C.: Improving machine learning approaches tocoreference resolution, In Proceedings of the 40th Annual Meetingon Association for Computational Linguistics, pp. 104–111 (2001).[6] Poesio, M., Mehta, R., Maroudas, A. and Hitzeman, J.: Learningto resolve bridging references, In Proceedings of the 42nd AnnualMeeting on Association for Computational Linguistics, pp. 144–151(2004).[7] Soon, W. M., Ng, H. T. and Lim, C. Y.: A Machine Learning Approachto Coreference Resolution of Noun Phrases, ComputationalLinguistics, Vol. 27, No. 4, pp. 521–544 (2001).[8] Vapnik, V. N.: The Nature of Statistical Learning Theory (1995).[9] Vieira, R. and Poesio, M.: An Empirically Based System for ProcessingDefinite Descriptions, Computational Linguistics, Vol. 26,No. 4, pp. 539–593 (2000).[10] , , , : Wikipedia , 14 , pp.769–772 (2008).[11] , : , . NLC, , Vol. 95, No. 168,pp. 43–50 (1995).[12] , , , : , , Vol. 46, No. 3, pp.831–844 (2005).[13] , , , : NAIST :, . , No. 2007-NL-177, pp. 71–78 (2007).