The Research on Syntactic Features in Semantic Role Labeling

Transcription

1 J OU RNAL OF CH IN ESE IN FORMA TION PROCESSIN G Vol. 23, No. 6 Nov., 2009 : (2009) ,,,, (, ) :,,,( NULL ),,,; CoNLL22005 Shared Task WSJ % %F1, : ;;;; ; : TP391 : A The Research on Syntactic Features in Semantic Role Labeling L I J unhui, WAN G Hongling, ZHOU Guodong, ZHU Qiaoming, QIAN Peide ( School of Computer Science & Technology, Soochow University, Suzhou, Jiangsu , China) Abstract : A feature2based semantic role labeling system operated on signal syntactic parse is constructed. The sys2 tem is divided into three sequential tasks : (1) filtering out constituent s that represent no semantic argument s with high probabilities, (2) classifying constituent s of candidate semantic argument s into the specific categories (inclu2 ding NULL class), and (3) dealing with overlap argument s and constituent s all labeled as core2argument s in the post2processing step. Besides combining and optimizing the existing features presented in other work, the paper ex2 tract s new features according to knowledge of grammar, pattern and collocation. The experiment s show the effec2 tiveness and robustness of the new extracted features, with which the finally SRL system achieves F1 value % and % on the development and WSJ test set respectively. As far as we know, it is the best result based on sin2 gle syntactic parsers on the CoNLL22005 Shared Task. Key words : artificial intelligence ; natural language processing ; semantic role labeling ; grammar2driven feature ; pat2 tern feature ; collocation feature 1,,, ( Argent ) ( Patient ) ( In2 (Semantic Role Labeling, SRL),, CoNLL 2004 [1 ] 2005 [ 2 ] SRL,SRL, strument),,(locative) : : : 863 (2006AA01Z147) ; ( ) ; (08 KJD520010) : (1983 ),,, ; (1975 ),,, ; (1967 ),,,,

2 ( Temporal) (Manner) ( Cause) 1 PropBank,, Arg0 ; Arg1 ;ArgM2LOC, 1 YaoMing plays basketball in NBA plays CoNLL,,SRL, ;, ( Argument Identification), (Argument Classification),, ( ) (),,, ;,,,;, CoN2 LL2005,, WSJ F % %, : ; Baseline ;, ;;,, [ 3 ], 7,( Constit uent Type) ( Subcategorization) ( Parse Tree Pat h) ( Constit uent Posi2 tion) ( Predicate Voice ) (Constituent Head Word) ( Predicate) 7 SRL [4 ],7,, ( Head POS) PP ( First Word in Constit2 uent) [ 5 ]7, 5,, [ 6 ] PA K ( Predicate/ Argument Structure Kernel) [ 7 ] PA K, ( Pat h Kernel) (Constituent Struct ure Kernel), [8 ],, buy a car buy a red car high degree higher de2 gree,,,,, ;,,,,,, 3 Baseline ,: 1)

3 6 : 13, (NULL vs. NON2NULL),( Null ) ( = 0. 9), ;2), NULL ;3),,,, P (NULL ) >, ; (NULL ),, ;,, SVMLight,one vs. ot hers ( NULL ) (4 ) ( CBaseFeature) (15 ) 7 [4, ] [4, ] [4, ] 3. 2 [4, ], 0,, [12 ] N P S V P VB ( [14 ], ) N P,;N P, N P S V P VB N P, N P N P,,, SRL, 1 2,Baseline 1 ( IBaseFeature), [4, ] [425, 12214], Collins (6 ) V P, V P2 > VBZ_NP_PP,,, 1, N P ( Yaoming) play N P S V P VBD N P, SBAR [14 ] (12 ) ,, C1 C2 ( 1 N P (NBA) PP (in NBA) ),,,,, NULL The OpenNL P Maximum Entropy Package. http :/ / max2 ent. sourceforge. net/, PP PP +,PP + in ; + PP + NBA

4 ,, SRL,,, : C1 C2, C1 A B,C2 A C p (A C1) p (C C2) > p (B C1) p (A C2) C1 A, C2 C,,C1 B,C2 A S N P + V P, S A,B C,,SBAR (SBAR (t hat) SBAR ( since) ) ;c. A A CC A, (A, CC,and, or ), A ; d. S 2 S,2 (a), ;2 (b), 2 (c), S, S S, : S N P +,S ( TO + ) V P +,S 4 SRL Baseline, : 1) SRL,,,,;,, ;2),, ;,Baseline, 4. 1 VBN ;b. SBAR SBA R,, 2 S 2) V P, 1 N P (basketball), ( 1),, 3 (a), buying Big in2 vestment banks (ref used, step support) (by) N P (Big in2, vestment banks) buy, N P S V P S V P V P S V P V P PP, S V P VB G, 1) N P buy, : a. ref used step support buying,,, N P (Big, 1 YaoMing invest ment banks) ;, V P (buy2 played basket ball in NBA,play ing... ) V P (ref used to.. ) ( 3 (b), ),N P buy VB VBZ VBD VBP G VBN (,,ref use step have ), support,,

5 6 : 15,, 3 N P(Big investment banks) VB G(buying) 4. 2, ( Pattern), Sbody benefit Sbody St hing,sbody benefit St hing f rom Sbody/ St hing benefit,,, ( A0, A1 ) open N P1 open N P2 open N P3, open, N P1,the store finally opened ;N P2 N P3,I opened t he box, go come take,,,,,, 4, 4 (a) N P ( I) 2 = N P V (A) N P,,,, go t hrough come up with take place take over 4 ( b) N P ( The, 4,,( Collocation), (4 ), : ( ) 4 (a) 4 NP(I) 1 = NP V (A) NP, 1,(A),V event) 3 = N P V ( A ) place CoNLL2005

6 , PropBank Brown : PropBank [9 ] Section02221, ; Section24,1 346 ;Section23,2 416 ;, Brown 426,, CoNLL2005 [ ], % % [ 14 ] Char2 niak Collins, Charniak, srl2eva. pl, F1, 3, 100, SVMLight c = e = m = , 5. 2, 3,P ( NULL ) >(, 0. 9),; 3 CoNLL 2005 Shared Task TestWSJ (= 0. 9) Precision Recall F1 IBaseFeature IBaseFeature IBaseFeature IBaseFeature + Both ,, S SBAR,,;,,, 3,,, ( Precision % %), ( IBaseFeat ure + ) : 1), ;2) IBaseFeat ure, NULL,( IBaseFeature + ) 5. 3 SRL,, P (NULL) >, ; ( NULL ) 4 CoNLL 2005 Shared Task Test WSJ, 4 CoNLL 2005 Shared Task TestWSJ SRL,= 0. 9, IBaseFeature Precision Recall F1 CBaseFeature CBaseFeature CBaseFeature CBaseFeature CBaseFeature CBaseFeature CoNLL 2005 Shared Task Test WSJ, Baseline,, 5 : 1), S SBAR,, ( F %), ( F %) Bot h +,

7 6 : 17 5 CoNLL 2005 Shared Task TestWSJ SRL (= 0. 9) IBaseFeature IBaseFeature + CBaseFeature CBaseFeature CBaseFeature + CBaseFeature + CBaseFeature + Both Precision Recall F ),,F % 3), SRL, Baseline % % 5. 4 SRL Punyakanoc [ 12 ] CoNLL 2005 Shared Task,,, (Charniak),Surdeanu [13 ] [14 ] Pradhan,,,, ( Charniak), 6 Baseline Baseline + Bot h 6 : 1) Baseline [425,14 ],, [13214 ] 2) SRL, ( WSJ + Brown) F ) Brown SRL WSJ, F %, :, SRL ;, SRL, 4) CoNLL 2005 Shared Task, Brown,, 6, ;, ; Overlap, Baseline ;, CoNLL 2005 Shared Task,, 6 SRL Development Test WSJ Test Brown Test WSJ + Brown P R F1 P R F1 P R F1 P R F1 Punyakanoc et al., Surdeanu et al., , Baseline Baseline + Both

8 ,, The store opened last week, open N P open N P, The store A0,,,, (), : 1),CoNLL 2005 Shared Task ,3 101, % ; 1 795, 26 % ;2),, ;,, anes2 t hetic () antibiotic (),,, : [1 ] Carreras X. and M rquez L. Introduction to the CoN2 LL22004 Shared Task : Semantic Role Labeling [ C ]/ / Proceedings of CoNLL 2004 Shared Task [2 ] Carreras X. and M rquez L. (2005). Introduction to the CoNLL22005 Shared Task : Semantic Role Labeling [ C]/ / Proceedings of CoNLL 2005 Shared Task [3 ] Gildea D. and J uraf sky D. (2002). Automatic Labe2 ling of Semantic Roles [J ]. Computational Linguistics, 2002, 28 (3) : [4 ] Pradhan S., Hacioglu K., Krugler V. et al. (2005). Support Vector Learning for Semantic Argument Clas2 sification [ J ]. Machine Learning Journal, 2005, 60 (3) : [5 ] Xue N. and Palmer M. (2004). Calibrating Features for Semantic Role Labeling [ C ]/ / Proceedings of EMNL P, [6 ] Moschitti A. (2004). A Study on Convolution Kernels for Shallow Statistic Parsing [ C ]/ / Proceedings of ACL22004,2004 : [7 ] Che W., Zhang M., Liu T. and Li S. (2006). A Hy2 brid Convolution Tree Kernel for Semantic Role Labe2 ling [ C ]/ / Proceedings of the COL IN G/ ACL 2006 Main Conference Poster Sessions, 2006 : [8 ] Zhang M., Che W., AW A. T. et al. (2007). A Grammar2driven Convolution Tree Kernel for Semantic Role Classification [ C ]/ / Proceedings of ACL22007, 2007 : [9 ] Palmer M., Gildea D. and Kingsbury P. The Proposi2 tion Bank : An Annotated Corpus of Semantic Roles [J ]. Computational Linguistics, 2005, 31 (1). [10 ] Charniak E. A Maximum2entropy Inspired Parser [ C]/ / Proceedings of NAACL22000,2000. [11 ] Collins M. ( 1999). Head2driven Statistical Models for Natural Language Parsing [ D ]. Ph. D. thesis, University of Pennsylvania. [12 ] Punyakanoc V., Koomen P., Roth Dan, and Yih W. (2005). Generalized Inference With Multiple Seman2 tic Role Labeling Systems [ C]/ / Proceedings of CoN2 LL22005,2005. [13 ] Surdeanu M. and Turmo J. Semantic Role Labeling Using Complete Syntactic Analysis [ R]/ / Proceedings of CoNLL22005,2005. [14 ],,. (2007). [J ]., 2007,18 (3) :