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1 1 2 Compositional Translation Estimation of Technical Terms Using a Domain/Topic-Specific Corpus Collected from the Web Masatsugu Tonoike,TakehitoUtsuro and Satoshi Sato This paper studies how to compile a bilingual lexicon for technical terms using the Web. In the task of estimating bilingual term correspondences of technical terms, it is usually rather difficult to find an existing corpus for the domain of such technical terms. In this paper, we adopt an approach of collecting a corpus for the domain of such technical terms from the Web. As a method of translation estimation for technical terms, we employ a compositional translation estimation technique, where translation candidates of a term are compositionally generated by concatenating the, Graduate School of Informatics, Kyoto University, Graduate School of Systems and Information Engineering, University of Tsukuba, Graduate School of Engineering, Nagoya University

2 Vol. 14 No. 2 Apr translation of the constituents of the term. Then, the generated translation candidates are validated using the domain/topic-specific corpus collected from the Web. This paper further quantitatively compares the proposed approach with another approach of validating translation candidates directly through a search engine. We show that the domain/topic-specific corpus collected from the Web contributes to achieving higher precision in translation candidate validation. Key Words: Compsitional translation estimation, Web, Echnical term 1 1 (Matsumoto and Utsuro 2000) 2 (Fung and Yee 1998; Rapp 1999) 2 ( 2000; Baldwin and Tanaka 2004) Y ST 34

3 88.5% (Cao and Li 2002) ( 2000) 12 35

4 Vol. 14 No. 2 Apr S T 1 XS U 2 X SM synapse transplanted liver sensory cell vastus intermedius oculomotor nerve osseous system pleural mesothelium skin capillary palatal mucosa 1 indusium : mother liquor : calcar : X SU sensory cell : transplanted liver : oculomotor nerve : Y S X T U osseous system pleural mesothelium skin capillary X TU indusium : mother liquor : calcar : osseous system : pleural mesothelium : skin capillary : 36

5 XS M Y S 3 1 XS U XS U XU T Y S XS M regista register (Tonoike, Kida, Takagi, Sasaki, Utsuro, and Sato 2005) 69% 75% XS M 2.2 T 1 XT U XU T xu T x U T X U T x U T xu T xu T x U T xu T x U T AND what s xu T AND glossary xu T. xu T 2 goo ( Yahoo! ( 37

6 Vol. 14 No. 2 Apr ( 2005) ( 2005) ( 2005) a application(1) practical(0.3) applied(1.6) action(1) activity(1) behavior(1) analysis(1) diagnosis(1) assay(0.3) b application(1) practical(0.3) applied(1.6) behavior analysis(10) applied behavior analysis(17.6) ( )+(1.6 10) application behavior analysis(11) applied behavior diagnosis(1) 2 38

7 3 2 a b 2 a b applied behavior analysis a b a applied behavior analysis applied behavior analysis =1.6 b applied behavior analysis applied behavior analysis = 16 applied behavior analysis = ( 2000) 3 applied: 1 applied 1 4 applied: 2 P 2 P 2 3 Ver.79 ( JUMAN ( 39

8 Vol. 14 No. 2 Apr applied mathematics : applied science : applied robot :. applied : :40 3 B P P 2 B S B P B S 2 B P B S ( 2000) B P B S B B P B S B P applied B P B S B 4.1 B B P B S 2% 5 B 1% B P,B S 1% B P,B S 1% % B P,B S DF-CO 40

9 1 1,292,117 1,228,750 1,671,230 P 2 217, , ,979 B P 37,090 34,048 95,568 B S 20,315 19,345 62,419 B 48,000 42, ,848 : (Ver.79) P 2 : 2 B P : B P B S : B S B : B 1 P 2 B P,B S,B y S S w i S w i y S w i y S = w 1,w 2,...,w m (1) y S w 1 = w 2 = w 3 = y S y S s j y S = w 1,w 2,...,w m s 1,s 2,...,s n (2)

10 Vol. 14 No. 2 Apr s j w i y S 3 7 s 1 = s 2 = s 1 = s 2 = s 3 = s 1 = s 2 = (3) y S applied behavior analysis 3 s 1 = applied, s 2 = behavior analysis s 1 = applied, s 2 = behavior,s 3 = analysis s 1 = applied behavior, s 2 = analysis (4) s i t i y S y T y S 8 y T = t 1,t 2,...,t n (5) y T y S y T y T s i,t i q( s i,t i ) y T y T Q corpus (y T ) y T 2 n q( s i,t i ) Q corpus (y T ) (6) i=1 q Q corpus y S 3 8 angle of radiation of of ff angle of radiation radiation angle j of optical-fiber cable 42

11 (3), (4) y S (6) y T n Q(y S,y T )= q( s i,t i ) Q corpus (y T ) (7) y S=s 1,s 2,...,s n i=1 y S = y T = applied behavior analysis y S,y T applied behavior analysis behavior analysis y T y S y T s 1 = s 2 = s 3 = applied behavior analysis s 1 = s 2 = applied behavior analysis y S,y T 2 (1) y S y S = w 1,w 2,...,w m w i 0,...,m y S = 0 w 1 1 w 2 2 m 1 w m m (8) i j w j,k w j,k w j+1,w j+2,...,w k (9) w 0,0 ε ε y 1 εy = y y S k 43

12 Vol. 14 No. 2 Apr w 0,k Tran(w 0,k ) y S = w 0,m = w 1,...,w m Tran(y S = w 0,m ) 1 Tran(w 0,k )(k =1,...,m) Tran(w 0,k )=top( merge( k 1 r ) i=0 concat(tran(w 0,i ), tran(w i,k ))), (10) w 0,k = w 1,...,w k i w 0,i = w 1,...,w i w i,k = w i+1,...,w k 2 i i =0 i = k 1 w 0,i Tran(w 0,i ) w i,k w i,k tran(w i,k ) concat merge top r Tran(w 0,k ) (10) 2 y S = Tran(y S = w 0,3 ) r 3 (10) i =0,...,k 1 (i =0) w 0,3 w 0,0 = ε w 0,3 = w 0,3 = concat (i =1) w 0,3 w 0,1 = w 1,3 = Tran(w 0,1 ) w 0,1 tran(w 1,3 ) w 1,3 concat Tran(w 0,1 )={ application, practical, applied } tran(w 1,3 )={ behavior analysis } application behavior analysis practical behavior analysis applied behavior analysis (i =2) w 0,3 w 0,2 = w 2,3 = Tran(w 0,2 ) w 0,2 tran(w 2,3 ) w 2,3 concat Tran(w 0,2 )={ applied action, applied activity, applied behavior } tran(w 2,3 )={ analysis, diagnosis, assay } 44

13 applied action analysis applied action diagnosis applied action assay applied activity analysis applied activity diagnosis applied activity assay applied behavior anaylysis applied behavior diagnosis applied behavior assay y S merge top r y S y T 3.3 y T s i,t i q s i,t i q 2 - (DF) natural language processing natural, language, processing, 3 natural language, processing, 1 P 2 45

14 Vol. 14 No. 2 Apr (a) 1 1 (b) P 2 (c) (a) (b) (c) 3 (a) (b) (c) (a) (b) (c) (b) (c) (c) P 2 (c) (b) (c) (b) (c) (b) s, t q( s, t ) 10 (compo(s) 1) ( s, t in ) q( s, t ) = log 10 f p ( s, t ) ( s, t in B P ) (11) log 10 f s ( s, t ) ( s, t in B S ) compo(s) s f p ( s, t ) P 2 s, t f p ( s, t ) P 2 s, t (11) P (11) DF 46

15 (DP) ( 2000) y S y T P (y S y T ) P (s i t i ) ( 2000) B B B B 10% B P (s i t i ) B q( s, t ) =P (s t) = 10 ( s, t in ) f prob ( s j,t ) = f B ( s j,t ) ( s, t in B) f prob( s,t ) Ps j f prob ( s j,t ) (12) 10 9 (12), (13) DP (13) y T (CF): y T Q corpus (y T )=freq(y T ) (14) (CP): y T ( 2000) t i t i t i DF ( 2000) B (15) t i 1 47

16 Vol. 14 No. 2 Apr n 1 Q corpus (y T )=P (t 1 ) P (t i+1 t i ) (15) i=1 (CO): y T 1 y T Q corpus (y T )= 0 y T (16) p(prune) (10) top f(final) (DF) (DP) (CF) (CP) (CO) DF-CF p/f p/f o DF-CF f p/f f o DF-CP p/f p/f o DF-CO p/f p/f o DF-CO f p/f f o DP-CF p/f p/f o DP-CP p/f p/f o CF p/f o CP p/f o DF-CF f -w p/f f o DF-CO f -w p/f f o DF p/f p(prune): f(final): 48

17 DF-CO DF CO (10) top DF-CO f f DF-CO f -w -w 3 DF P (s t) DP 2 CF CP CO DF DP DF DF 11 DF CF CP CO 3 DP CF CP DP-CP ( 2000) DF CP CF CO DF-CF f -w DF-CO f -w

18 Vol. 14 No. 2 Apr DF-CF f DF-CO f DF 12 CF CP S, T 3 4 ( 1998) 3 Y S S = S = X U S MBytes X U S MBytes DF F DP 13 CO CO

19 ( 1990) ( 2001) 25 ( AI 1996) X ST 2 t e,t j Y ST t e t j t e t j t e 2 t j 2 t e t j 10 S = S = X ST XS U 2.2 XU S XT U S = S = Y ST Y S Y S Y ST S = 1.31 S = XS U Y S Y S Y S S % % S % % Y S S 90.6% S 92.5% 14 Y S Y S 4 T g T c T gc T g B P B S 3.3 r T c T gc Y ST Y ST 1 S 51

20 Vol. 14 No. 2 Apr Y S T g T c T gc t Y S,S(t) t T g = {t s S(t),s } T c = {t s S(t),s } T gc = {t s S(t),s s } 4 Y S B P B S T gc 15 T g T c T gc T g T c T g T c T gc 4 B P B S 71.8% 75.7% 50.4% 56.6% B P B S T gc 53.8% 51.9% 15 CP T g P (t 1 ) P (t i+1 t i ) 52

21 4 B P,B S (T g) (T c) (T gc) (T g) (T c) (T gc) 60% 93% 57% 87% 90% 73% 71% 78% 59% 71% 68% 51% 61% 65% 35% 71% 68% 52% 86% 93% 79% 82% 100% 82% 75% 96% 71% 82% 86% 75% 74% 52% 34% 80% 61% 46% 78% 92% 74% 80% 55% 45% 69% 83% 55% 76% 70% 56% 54% 63% 39% 56% 54% 36% 85% 71% 58% 80% 61% 53% 71.8% 74.9% 53.8% 75.7% 65.3% 51.9% 5 B 60% 87% 80% 78% 61% 71% 86% 82% 79% 82% 75% 81% 79% 81% 71% 78% 55% 57% 86% 81% 73.6% 77.0% B 5 B 3.2 B P,B S 53

22 Vol. 14 No. 2 Apr of Y ST 617 of T g 27 2 T g 7 T g of Y S r 10 goo Yahoo! Y S 6 top 1 1 top Y S Y S top 1 top 10 top 1 top 10 DF-CF 42.5% 50.1% 41.8% 48.1% DF-CF f 38.2% 41.5% 39.5% 44.1% DF-CP 43.6% 52.4% 44.6% 51.9% DF-CO 44.7% 47.6% 43.9% 48.6% DF-CO f 39.9% 41.7% 39.7% 44.1% DP-CF 46.0% 54.3% 44.7% 51.5% DP-CP 46.7% 56.2% 48.9% 56.1% CF 26.3% 48.0% 31.3% 43.8% CP 25.9% 48.6% 32.6% 46.5% DF-CF f -w 52.0% 59.0% 51.1% 65.8% DF-CO f -w 44.1% 59.0% 50.1% 65.0% DF 35.7% 59.0% 45.1% 63.2% 54

23 7 1 (a) 1 10 F F DF-CF % 51.7% % 61.0% DF-CF f % 51.3% % 55.7% DF-CP % 51.5% % 61.8% DF-CO % 55.4% % 59.0% DF-CO f % 53.5% % 55.9% DP-CF % 53.0% % 62.5% DP-CP % 51.8% % 62.4% CF % 30.2% % 55.2% CP % 28.7% % 53.9% DF-CF f -w % 58.5% % 66.3% DF-CO f -w % 49.5% % 66.3% DF % 37.4% % 61.9% (b) 1 10 F F DF-CF % 52.2% % 60.1% DF-CF f % 52.2% % 58.2% DF-CP % 53.1% % 61.8% DF-CO % 55.0% % 60.9% DF-CO f % 52.5% % 58.2% DP-CF % 52.8% % 60.9% DP-CP % 54.6% % 62.6% CF % 36.9% % 51.7% CP % 36.4% % 51.9% DF-CF f -w % 55.3% % 71.3% DF-CO f -w % 54.3% % 70.4% DF % 47.0% % 66.0% F 6 55

24 Vol. 14 No. 2 Apr Y S T c 74.9% 65.3% DF-CO f 79.3% DF-CF f -w 63.5% F DP-CO 55.2% DF-CF f -w 56.9% F 1 DF-CO f 2 DF-CF f DF-CO f DF-CF f 2 F F DF DP 56

25 DF-CF DP-CF DF-CP DP-CP DP 4 5 DF DP DF F DF DP CF CP CO 3 DF-CF DF-CP DF-CO DF-CF DF-CO DF-CO CF DF-CO DF CP CP CF-CP DP-CP CP CP ( 2000) DP-CP F DF F CF CP F of DF-CO

26 Vol. 14 No. 2 Apr Y S F DF-CO 8 4 T gc T gc 9 1 DF-CO 8 Y S 9 80% 1 T gc 8 DF-CO top 1 top 10 top 1 top 10 40% 47% 60% 63% 46% 49% 44% 46% 32% 32% 42% 48% 68% 71% 75% 82% 61% 64% 57% 71% 32% 32% 38% 43% 52% 57% 36% 41% 48% 49% 50% 52% 35% 37% 33% 35% 51% 56% 43% 51% 44.7% 47.6% 43.9% 48.6% 58

27 9 1 F : DF-CO (a) 1 10 F F % 52% 14 88% 61% % 57% 20 77% 60% % 44% 10 71% 44% % 72% 20 80% 75% % 69% 18 86% 73% % 42% 32 60% 42% % 61% 52 81% 67% % 59% 42 78% 60% % 48% 31 86% 52% % 59% 55 79% 65% % 55.4% % 59.0% (b) 1 10 F F % 71% 19 90% 75% % 54% 19 73% 57% % 55% 15 94% 64% % 79% 23 92% 87% % 64% 20 91% 80% % 47% 43 69% 53% % 46% 37 73% 52% % 63% 45 88% 66% % 47% 29 85% 49% % 54% 50 82% 63% % 55.0% % 60.9% Y S 6 ATLAS 2003 The V6.0 IBM Version

28 Vol. 14 No. 2 Apr ATLAS % 38.1% CF CP 4.3 B P B S 4 (T g ) % 31% Y S 9.4% 7.5% 42% 44% 3% 10% 10% 9% 4.4 DF-CO DF-CF f -w F DF-CO DF-CF f -w DF-CO T gc T g T g T gc T g T gc T g T gc T g 1 T gc 1 60

29 10 Y S (a) 58 33% 73 42% 6 3% 6 3% 18 10% 2 1% 1 1% 2 1% 1 1% 6 3% 1 1% % (b) 46 31% 66 44% 15 10% 4 3% 13 9% 2 1% 1 1% 1 1% 1 1% 1 1% % 11 DF-CO Y S 1 T gc T g T gc T g Y S

30 Vol. 14 No. 2 Apr DF DF-CO DF-CF f -w DF-CO DF-CO T gc % 10 20% 25 45% 10 20% 18 32% 29 59% % % 13 DF-CO DF-CF f -w DF-CO

31 354 Y S 54.9% 57.4% % 67.4% DF-CO F 61.1% 62.0% DF-CO DF-CF f -w 2 ( 2006) ( 2000) DP-CP DP-CP Y S 1 F ( 2000) ( 2000) 65 ( 2000) ( 2004) NTCIR-1(Kando, Kuriyama, and Nozue 1999) NTCIR-2(Kando, Kuriyama, and Yoshioka 2001) ( 2000) χ 2 2 (Baldwin and Tanaka 2004) 63

32 Vol. 14 No. 2 Apr SVM (Baldwin and Tanaka 2004) (Baldwin and Tanaka 2004) Reuters Corpus (Baldwin and Tanaka 2004) 2 2 (Cao and Li 2002) (Cao and Li 2002) (Cao and Li 2002) 2 F (Cao and Li 2002) 2 ( 2000) OR ( 2000) ( 2004) Yahoo! 64

33 6 1 2 F (Nagata, Saito, and Suzuki 2001; Huang, Zhang, and Vogel 2005) (Knight and Graehl 1998; Oh and Choi 2005) ( 2006) ( 2006) ( 2006) 65

34 Vol. 14 No. 2 Apr (2004).., 45 (SIG10(TOD23)), pp Baldwin, T. and Tanaka, T. (2004). Translation by Machine of Compound Nominals: Getting it Right. In Proc. ACL 2004 Workshop on Multiword Expressions: Integrating Processing, pp (2006).. 12, pp Cao, Y. and Li, H. (2002). Base Noun Phrase Translation Using Web Data and the EM Algorithm. In Proc. 19th COLING, pp (2001)... (2000).., 41 (4), pp Fung, P. and Yee, L. Y. (1998). An IR Approach for Translating New Words from Nonparallel, Comparable Texts. In Proc. 17th COLING and 36th ACL, pp Huang, F., Zhang, Y., and Vogel, S. (2005). Mining Key Phrase Translations from Web Corpora. In Proc. HLT/EMNLP, pp AI (1996) Kando, N., Kuriyama, K., and Yoshioka, M. (2001). Overview of Japanese and English Information Retrieval Tasks (JEIR) at the Second NTCIR Workshop. In Proc. 2nd NTCIR Workshop Meeting, pp Kando, N., Kuriyama, K., and Nozue, T. (1999). NACSIS test collection workshop (NTCIR-1). In Proc. 22nd SIGIR, pp (2006).., J89-D ( ). (2004). Web., 45 (SIG7(TOD22)), pp Knight, K. and Graehl, J. (1998). Machine Transliteration. Computational Linguistics, 24 (4), pp (2000). Web., 41 (SIG6(TOD7)), pp Matsumoto, Y. and Utsuro, T. (2000). Lexical Knowledge Acquisition. In Dale, R., Moisl, H., 66

35 and Somers, H. (Eds.), Handbook of Natural Language Processing, chap. 24, pp Marcel Dekker Inc. (1998)... (1990)... Nagata, M., Saito, T., and Suzuki, K. (2001). Using the Web as a Bilingual Dictionary. In Proc. Workshop on Data-driven Methods in Machine Translation, pp Oh, J. and Choi, K. (2005). Automatic Extraction of English-Korean Translations for Constituents of Technical Terms. In Proc. 2nd IJCNLP, pp Rapp, R. (1999). Automatic Identification of Word Translations from Unrelated English and German Corpora. In Proc. 37th ACL, pp (2006).., 13 (3), pp (2005).. 11, pp Tonoike, M., Kida, M., Takagi, T., Sasaki, Y., Utsuro, T., and Sato, S. (2005). Effect of Domain- Specific Corpus in Compositional Translation Estimation for Technical Terms. In Proc. 2nd IJCNLP, Companion Volume, pp

36 Vol. 14 No. 2 Apr

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