Language Information RetrievalÓ j

Transcription

1 ± i jschang@cs.nthu.edu.tw o ± k j ± j z ± wyw j ± j ÒCross Language Information RetrievalÓ j h ±ÒQuery TranslationÓ ± k h ± ± ± k ± h ± j ± ± ± 1. ± j y k j ± j z ± y p wywj ± 1

2 j ÒCross-Language Information RetrievalÓ q z ± ± j ä ±åòdocument translationó ä hi ±åòquery translationó ÒMcCarley 1999Ó h ± NTCIR-2 i ÒKando et al. 2001Ó h h Assembly Parade Law Parade and Demonstration Constitution Freedom of speech Indemnification Communism Country separation Council of Grand Justices Legislation Amendments h ± dg ÒWord Sense DisambiguationÓ ÒIde and Veronis 1998, Chen and Chang 1998Ó ±ÒPhrase TranslationÓ ± ± ÒText CollectionÓ ± d g ± ÒLexical ChoiceÓ demonstration ± ä å ± ä å 2

3 j u 1. ± ÒGey and Chen 1997, Kwok 2001Ó 2. ÒOard 1999, Kwok 2001Ó u ± ± Kwok ± Michael Jordan ±ä å j ± Chen Ò1999Ó Òoccurrence statisticsó ± µ ± j ± ÒKnight and Graehl 1997, Chang et al. 2001Ó j j p j h ± ± ± ÒExample-based ApproachÓ ± ji Òdata-drivenÓ j i ± IBM Watson Brown Ò1988, 1990, 1993Ó ± j p j k u ± ÒStatistical Alignment and Machine TranslationÓ h ± j h ± j g u ± ÒAlignment ProbabilityÓ 3

4 ± 2. ± ± ± ÒDirect ApproachÓ r ÒTransfer ApproachÓ 1980 j ÒEmpirical ApproachÓ ± ± Brown p Brown S ± T ± ÒTranslation ProbabilityÓ Pr( T S ) g (a) ± ÒLexical Translation ProbabilityÓ Pr( S i T j ) (b) ÒFertility ProbabilityÓ Pr( a b ) (c) ÒDistortion ProbabilityÓ Pr(i j, k, m ) S i S i T j T j a S i b T j k S m T Brown ± k ä å ä åqv ÒExpectation and Maximization 4

5 AlgorithmÓ ä å º ± ä å º ± k EM qv ± ± k ÒNoisy Channel ModelÓ ± N-gram ÒLanguage ModelÓ qv ÒBeam SearchÓ ± 3. ± Brown ± ± ÒalignmentÓ y ± ± S i, i i T j S i j 1 Pr(j i, k, m) 1 Pr(j i, k, m) =0,i i k ± ± A* S 1 S 2 S 3 ± S 1 {T 1, T 2 } S 2 {T 3, T 4 } S 3 {T 5 } A* = (0, 12, 34, 5)Ò 0 Ó k =3 m =5 ± A* 35% A* ÒMaximum Likelihood EstimationÓ 5

6 Pr MLE ( A* )=0.35 Pr( A* )=P(1 1,3,5) P(2 1,3,5) P(3 2,3,5) P(4 2,3,5) P(5 3,3,5) j Ò0.6Ó P(j i,3,5) k Pr( A* ) < (0.6) 5 = << 0.35 Pr(A*)<<Pr MLE ( A* ) w ± ± ± ÒAlignment ProbabilityÓ ± ± S ± T ± PrÒT SÓ g (a) ± ÒLexical Translation ProbabilityÓ Pr( T(A i ) S i ) (b) ± ÒAlignment ProbabilityÓ Pr ( A k, m )=Pr ( A 0, A 1, A 2,,A k k, m ) S i S i T(A i ) T S i A 0 T S f A i T S i f, i>0 k S m T S i T 6

7 B(A, i) E(A, i) k S i T(A i )={T B(A,i), T B(A,i)+1, T E(A,i) } A i ={B(A,i), B(A,i)+1,, E(A,i) } 4. ± k g 1. ± j 2. ± u ± k kn 3. ± j k 4. ± j 4.1 BDCr ÒBDC 1992Ó 3 ± ± i i k 96,156 ± ÒP n, Q n Ó,n=1,N h EM qv ª ± ± Och Ò2000Ó 7

8 IBM 1. Brown EM qv ± 2. ± j jv Pr(j i, k, m )=1/m j 0.5 i 0.5 3I ( i j, k, m) = 1 [1] m k i = k = j = m = S T i k j M Pr( j i,k,m) flight flight flight flight eight eight eight eight ± 2 ± Pr( j i,k,m) Òflight eight, Ó 1 v 1 8

9 E C ± Pr (C E) Pr( C E) N k m n= 1 i= 1 j= 1 = N δ ( E, P ( i)) δ ( C, Q ( j)) Pr( j i, k, m) k m n= 1 i= 1 j= 1 n δ ( E, P ( i)) Pr( j i, k, m) P n (i) P n i Q n (j) Q n j k = P n m = Q n δ(x, y) =1 x = y, δ(x, y) =0 x y n n [2] S i T j i k j m Pr(j i,k,m) Pr(T j S i ) Pr(T j S i ) Pr(j i,k,m) flight flight flight flight eight eight eight eight ± 2 E C E Pr (C E) ± 9

10 4.2 EM qv v EM qv º ÒGreedy MethodÓ ÒP n, Q n Ó 0 ± Ò 2Ó º (threshold) k y ¼ flight eight 2 3 Ò0, 34, 12Ó S i T i i j k m Pr(j i,k,m) Pr(T j S i ) Pr(T j S i ) Pr(j i,k,m) flight flight eight eight Òflight eight, Ó Ò0, 34, 12Ó v - ± k 10

11 ± k m u A count( A ( S, T) ) Pr( A k, m) = [3] count( k = S, m = T ) 4 k m A A 0 A 1 A 2 Pr(A k,m) ± 15 EM qv 601 u 38 u ¼ 11

12 1. ± ± ± % y S T T(A 0 ) S 1 T(A 1 ) S 2 T(A 2 ) T-shaped antenna ƒ T-shaped antenna ƒ X-ray examination X-ray examination irresistible force irresistible force Unwritten law unwritten law Central Asia Central Asia mutual non-interference mutual non-interference undesirable element undesirable element unalterable truth unalterable truth come soon come soon a desperado a desperado 5 5 u v ± 4.2 ± g 12

13 Òbound morphemeó $empty$ i Òdata sparsenessó $any$ Good-Turing Òsmoothing methodó $any$ ± E C PrÒC EÓ flight flight flight flight $empty$ flight flight flight flight flight flight flight flight flight d flight $any$ flight ± 6 flight ± $empty$ $any$ flight $empty$ k ± 4 Ò0,0,1234Ó 13

14 Ò1,0,234Ó $any$ EM qv $empty$ r 4.3 EM qv v jv 1 ± S T A 0 A 1 A 2 T(A 0 ) T(A 1 ) T(A 2 ) Pr(T S,A) flight eight flight eight flight eight $empty$ flight eight flight eight Pr( flight eight) 5 jv ÒS, TÓ A v ± Pr(T S, A) A Pr(T S, A) A A (S i, T(A i )) Pr( T S) = max Pr( T S, A) = max Pr( A k, m) A A* A k i= 1 Pr( T ( A ) S ) i i 14

15 A * = arg max Pr( T S, A) A = arg max Pr( A k, m) A k i= 1 Pr( T( A ) S ) i i [4] k = S, m = T (S, T) =Òflight eight, Ó A ± v A = (0, 12, 34) Pr(T S,A) =Pr(0,12,34 2,4) Pr( flight) Pr( eight) A = (0, 34, 12) Pr(T S,A) =Pr(0,34,12 2,4) Pr( flight) Pr( eight) A = (0, 3, 124) Pr(T S,A) =Pr(0, 3, 124 2,4) Pr( flight) Pr( eight) A = (2, 34, 1) Pr(T S,A) =Pr(2, 34, 1 2,4) Pr( flight) Pr( eight) A = (12, 34, 0) Pr(T S,A) =Pr(12, 34, 0 2,4) Pr( flight) Pr($empty$ eight) 7 Òflight eight, Ó ± 7 A* = (0, 34, 12) 5. ± ± k 10 i EM qv j j 15

16 S T T(A 0 ) T(A 1 ) T(A 2 ) T(A 0 ) T(A 1 ) T(A 2 ) association football delay flip-flop I demodulator fg fg f g Disgraceful act disregard to secret ballot bearer stock false retrieval used car infix operation jv jv jv 8 jv ± ¼ ± j j 1. j n ± association ± ä å ä å association football ± ± äa å 2. ± ± Òlight verbó make take to 3. ± Òflight eight, Ó ¼ u j Brown u 16

17 8 Och (2000) 100 ( ) 2 u S (sure) P (possible) S P S P j (recall) (precision) (error rate) A Ι S recall = S 185 = = A Ι P precision = A 226 = = A Ι S + AΙ P AER( S, P; A) = 1 = 1 = A + S ± 6.1 ± ± i k i Òflight attendant Ó Òflight, Ó Òflight attendant Ó flight $any$ $any$ j 17

18 j œ~ d d œ~ Òflight, Ó ±Òflight, Ó Òflight, Ó ± Òflight, Ó 0 Òattendant, Ó ±Òattendant, Ó Òattendant, Ó Òattendant, Ó Òattendant, $any$ó ± j Òpreservation, Ó Òpreservation, Ó Òpreservation, Ó ± Òflight attendant Ó ± Pr($any$ flight) Pr($any$ eight) m ± Pr(A 2,3) u Ò0, 12, 3Ó Òflight, Ó Òattendant, Ó ± Òflight, Ó Òattendant, Ó j j Òflight, Ó Òattendant, Ó 18

19 LTP º k LTP j 6.2 ± ± ±j ± ± v j j h ± ± ± h ± u 1. ± Òdocument frequencyó ± ± 2. k k ± N-gram qv ± ± ª ± ± h S ± T* Pr( T S) = max Pr( T S, A) A 19

20 T * = arg max Pr( T S) Pr( T ) T = arg max max Pr( T S, A) Pr( T ) T = arg max max Pr( A k, m) T A A k i= 1 Pr( T ( A ) S ) Pr( T ) Pr(T) = ± T k = S m = T Pr(A k, m) Pr(T S) v qv ÒBranch and Bound AlgorithmÓ T* Pr(A k, *) Pr(* S i ) gòsolutionó Òupper boundó Pr(* S i ) Pr(T) N-gram k r g œ ± ƒ ± ÒKupiec 1993Ó ± k º P v ± A ± T j+1, j+m i i arg max Pr( A n( i), m) Pr( T A, j, m k= 1 j+ 1, j+ m = arg max Pr( A n( i), m) Pr( T A, j, m n( i ) = arg max Pr( A n( i), m) Pr( T A, j, m j+ 1, j+ m P, A) S b, e, A) j+ B( A, K ), j+ E( A, k ) 20 S b+ k 1 A n m )

21 Pr( A n, m) A S b+k-1 P k T j+b(a,k),j+e(a,k) P k ± A ± qv S T k (P 1, Q 1 ), (P 2, Q 2 ),,(P k, Q k ) (1). ÒPart of speech taggeró S ÒlemmaÓ (2). Òshallow parsingó Òbasic phraseó P 1, P 2,,P k P I = S b(i), e(i), P b =n(i) (3). P I v ± A ± T j ð (i) 1, j m*(i) ÒBranch and Bound AlgorithmÓ A*(i), j*(i), m*(i) argmax Pr( A n( i), m) A, j, m n( i) k = 1 Pr( T j+ B( A, k ), j+ E ( A, k ) S b+ k 1 ) (4). P I (P I, T j ð (i) 1, j m*(i) ) 7. ± ± h ± j 21

22 Brown v ±ª ƒ ± ± ± j h 1. ± 2. dg h œ ± 3. d Wordnet ± dg ƒf H g ~ ± 1. BDC 1992 The BDC Chinese-English electronic dictionary (version 2.0), Behavior Design Corporation, Taiwan. 2. Brown, P. F., Cocke J., Della Pietra S. A., Della Pietra V. J., Jelinek F., Mercer R. L., and Roosin P. S A Statistical Approach to Language Translation, In Proceedings of the 12th International Conference on Computational Linguistics, Budapest, Hungary, pp

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