Japanese Dependency Analysis Based on Improved SVM and KNN

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1 Proceedngs of the 7th WSEAS Internatonal Conference on Smulaton, Modellng and Optmzaton, Bejng, Chna, September 15-17, Japanese Dependency Analyss Based on Improved SVM and KNN ZHOU HUIWEI and YANG YAGE and YU TONG and HUANG DEGEN Department of Computer Scence and Engneerng Dalan Unversty of Technology DaLan, LaoNng P.R. CHINA Abstract: - Ths paper presents a method of Japanese dependency structure analyss based on mproved Support Vector Machne (SVM). Japanese dependency analyzer based on SVM has been proposed and has acheved hgh accuracy. The effcent way to mprove dependency accuracy farther s to ncrease the tranng data. However, the ncrease of tranng data wll brng a great amount of tranng cost and decrease the parsng effcency. We delete those samples that are unused or not good to mprove the classfer s performance, and then tran the reduced tranng set wth SVM to obtan the fnal classfer. Furthermore, we combne mproved SVM wth K nearest neghbors(knn) to mprove the performance of dependency analyzer. Experments usng the Unversty Corpus show that the method outperforms prevous systems as well as the dependency accuracy and the parsng effcency. Key-Words: - Japanese dependency analyss, Support Vector Machne(SVM), Improved SVM, Large tranng set SVM (LSVM), Nearest Neghbor-SVM (NN-SVM), K nearest neghbors(knn) 1 Introducton Dependency analyss has been recognzed as a basc process n Japanese sentence analyss. And a number of studes have been proposed. Japanese dependency s usually n terms of relatonshp between phrasal unts called bunsetsu segments (hereafter segments). In recent years, as large-scale tagged corpora have become avalable, a number of statstcal parsng technques usng such tagged corpora have been developed [1] [2] [3] [4]. The prevous dependency analyss s dvded nto two approaches. One approach s based on a statstcal model [1] [2] [3]. These models need to calculate the probabltes for all possble dependences n a sentence to obtan the optmal set of dependency. It s not effcent. The other approach s a cascaded chunkng model [4] based on SVM [5]. The method s smple and effcent. It acheves hgh accuracy. The further way to mprove accuracy s to ncrease the tranng data. However, the ncrease of tranng data wll brng a great amount of tranng cost. And the parsng effcency wll be affected as the number of support vectors ncreased. In the tranng data, there are many examples that are unused or not good to mprove the classfer s performance. If these examples can be deleted, the tranng cost wll be decreased and the analyss accuracy as well as effcency wll be mproved. Ths paper presents a method of Japanese dependency structure analyss based on mproved SVM. Frst, we tran an ntal classfer wth a small tranng set, and then prune the large tranng set wth the ntal classfer to obtan a small reducton set. And then, we prune the tranng set contnually, reserve or delete a sample accordng to whether ts nearest neghbor has same class label wth tself or not. Tranng wth the reducton set, fnal classfer s obtaned. Furthermore, we combne mproved SVM wth KNN to mprove the performance of dependency analyzer. Experments usng the Unversty Corpus show that the presented method outperforms prevous systems as well as mproves the parsng and tranng effcency. 2 Cascaded Chunkng Model Usng SVM 2.1 Support Vector Machne (SVM) Support Vector Machne (SVM) [5] s one of the bnary lnear classfers ntroduced by Vpnk. Suppose l tranng examples ( x, y ), (1 l ) are gven, where x s a feature vector n n dmensonal

2 Proceedngs of the 7th WSEAS Internatonal Conference on Smulaton, Modellng and Optmzaton, Bejng, Chna, September 15-17, feature space, y s the class label {+1, -1} (postve or negatve) of x. SVM fnds a hyperplane ( w x + b) = 0 whch separate the tranng examples and has maxmum margn between two hyperplane ( w x + b) 1 and ( w x + b) 1. The optmal hyperplane wth maxmum margn can be found by solvng the followng quadratc programmng problem. mn 1 2 w 2 (1) subject to y ( w x + b) 0, = 1,2,..., l The decson functon can be wrtten as: f ( x ) = sgn[ α y ( x x ) + b (2) x sv ] Where α s the Lagrange multpler correspondng to each constrant. The Kernel functon K ( x, xj) = φ( x) φ ( xj) can reduce the computatonal overhead when the tranng example x s projected onto a hgh dmensonal space by usng projecton functon φ. Among the many knds of Kernel functons, the d-th polynomal kernel: d K ( x s used. Where d s the, yj) = ( x xj + 1) dmenson of the polynomal functons. Further more, the optmzaton problem can be wrtten nto the followng maxmum problem. l l 1 (3) L ( α ) = α α α j y y j K ( x x j ) 2 = 1, j = 1 Fnally, the label of an unknown example s decded by the followng functon: f ( x ) = sgn[ α y K ( x x ) + b ] (4) x sv SVM estmate the label of an unknown example whether sgn of f ( x ) s postve(+1) or negatve(-1). 2.2 Japanese dependency Analyss Model We defne a sentence as a sequence of segments B = b1, b2,..., bm and ts syntactc structures as a sequence of dependency patterns D = dep( 1), dep(2),..., dep( m 1), where dep ( ) = j means that segment b depends on (modfes) segment b j. In ths frame-work, we suppose that the dependency sequence satsfes the followng constrans. 1. Except for the rghtmost one, each segment depends on exactly one of the segments appearng to the rght. 2. Dependences do not cross each other. In order to use SVM for dependency analyss, we adopt a sample method: We take a par of segments that are n a dependency relaton as a postve data, and a par of segments that are not n a dependency relaton as a negatve data. Japanese dependency analyss algorthm s as follows: 1. Put an O tag on all segments snce the dependency relaton of each one s undecded. 2. For each segment wth an O tag, decde whether t modfes the segment on ts mmedate rght hand sde. If so, the O tag s replaced wth a D tag. 3. Delete all segments wth a D tag that are mmedately followed by a segment wth an O tag. 4. Termnate the algorthms f a sngle segment remans, otherwse return to step 2 and repeat. 母は私にいろいろな物を買ってくれました (Mother bought many thng for me. ) 母は私にいろいろな物を買ってくれました Mother me many thng buy Intalzaton Input: 母は私にいろいろな物を買ってくれました Tag: O O O O O Input: 母は私にいろいろな物を買ってくれました Tag: O O D(Del.) D O Input: 母は私に物を買ってくれました Tag: O D(Del.) D O Input: 母は物を買ってくれました Tag: O D(Del.) O Input: 母は買ってくれました Tag: D(Del.) O Input: 買ってくれました O(Fnsh) Fg. 1. Example of the parsng process wth cascaded chunkng model Fgure 1 shows an example of the parsng process and the result. In tranng, the model smulated the parsng algorthm by consultng the correct answer from the tranng annotated corpus. In testng, the model consults the traned system and parses the nput sentence wth the parsng algorthm. The smplest and most effectve way to mprove accuracy s to ncrease the tranng data. However, the ncrease of tranng data wll brng a great amount of

3 Proceedngs of the 7th WSEAS Internatonal Conference on Smulaton, Modellng and Optmzaton, Bejng, Chna, September 15-17, tranng cost. And the support vectors of the traned model wll ncrease. Ths wll make the analyss tme too long to apply the traned model actually. In the tranng data, there are many examples that are unused or not good to mprove the classfer s performance. If these examples can be deleted, the tranng cost wll be decreased and the analyss accuracy as well as effcency wll be mproved. 3 Japanese Dependency Analyss Based on mproved SVM Several attempts have been made to mprove classfcaton accuracy of SVM by prune the tranng set [6] [7]. We adopt the approach called Large tranng set SVM (LSVM). It prunes the large tranng set wth the ntal classfer accordng to the dstance from a sample to the separate hyperplane. And then, we adopt the other approach called Nearest Neghbor-SVM (NN-SVM). It deletes a sample f ts nearest neghbor has dfferent class label wth tself. In ths paper, we use LSVM and NN-SVM (here after we call t NN-LSVM) to prune the tranng set. Then tran wth the reducton set to obtan fnal classfer. To mprove the performance of dependency analyzer further, we combne mproved SVM wth KNN. In the class phase, the algorthm computes the dstance from the test sample to the optmal hyperplane of SVM n feature space. If the dstance s greater than the gven threshold, the test sample would be classfed on SVM; otherwse, the KNN algorthm wll be used. 3.1 LSVM LSVM s a learnng strategy of SVM used to large tranng set. When we use a large tranng set, n order to reduce the computaton cost and do not affect the accuracy of the classfer, we prune the tranng data as follows: 1. Abstract a small tranng set S from a large tranng set L. Tran an ntal classfer wth the small tranng set S. The sze of the small tranng set S s decded under the followng condton: (1) The tranng cost s not bg when tranng wth t. (2) The accuracy of the classfer s adequacy when tranng wth t. 2. Prune the large tranng set L wth the ntal classfer to obtan a small reducton set. Then tranng wth the reducton set, fnal classfer s obtaned. The prune way s shown n fgure 2. The separate hyperplane of the ntal classfer s H. The dstance from a sample S to the separate hyperplane H s d( d 0). Reman the sample f 1 ε < d < 1+ ε. Otherwse, delete t. We can control the sze of the reduce set and the classfer accuracy by adjust the threshold ε ( 0 < ε < 1). In fact, the optmal classfer can be obtaned by adjust the thresholdε. the reman area 1+ε 1-ε 1-ε 1+ε H + H H the reman area Fg. 2. The prunng fgure of the LSVM Ths prune strategy s desgned along wth that the classfer s related wth the support vectors only and unrelated wth the other samples. After prunng wth ths strategy, the remaned samples are useful to classfcaton and the deleted samples are useless or even reactve to classfcaton. 3.2 NN-SVM SVM focuses on the samples near the boundary n tranng tme, and those samples ntermxed n another class are usually no good to mprove the performance of classer. On the contrary, they may greatly ncrease the cost of computaton and ther exstence may lead to overlearnng and decrease the generalzaton ablty. NN-SVM s an mproved SVM that can mprove the generalzaton ablty of classfer. It reserves or deletes a sample accordng to whether ts nearest neghbor has same class label wth tself or not, then trans the new set wth SVM to obtan a classfer. We prune the tranng data as follows: Fnd the nearest neghbor of each sample. If a sample has the same class wth ts nearest neghbor reserve t, otherwse delete t. The dstance between the two vectors x ( x = ( x 1, x 2, L, x n )) and x j ( x j = ( x j 1, x j 2, L, x j n )) s as: n k k 2 D( x, x ) = ( x x ) (5) j k= 1 j

4 Proceedngs of the 7th WSEAS Internatonal Conference on Smulaton, Modellng and Optmzaton, Bejng, Chna, September 15-17, SVM-KNN Analyzng the classfcaton results, we fnd that the msclassfed samples are manly near the hyperplane. Ths means that we should use the nformaton of the samples near the optmal hyperplane to mprove the performance of the classfer. Based on SVM theory, the samples near the optmal hyperplane are manly support vectors. In the meantme, the classfer based on SVM s regard as a 1NN classfer n whch only one representatve pont s selected for each class. Therefore, A SVM-KNN classfer combned SVM wth KNN s presented to mprove the performance of SVM classfer [8]. The algorthm of SVM-KNN s descrbed as follows: Suppose T s the testng set. (1) f T Φ, select x T, else stop; (2) compute the dstance g(x) from the test sample to the hyperplane of SVM as descrbed n (6); (3) f g(x) > ε, ε [0,1], output f (x) = sgn(g(x)), else use KNN algorthms and output the returned results; (4) T T {x}, go to step (1) The dstance from the test sample to the hyperplane of SVM n feature space s as: g ( x) = y α K( x, x) b x sv The dstance from each test sample to each reference pont s as: d ( x, x ) = φ ( x) φ( x ) = K( x, x) 2K( x, x ) + K( x, x ) (7) Where x s the support vector. 4 Experments And Dscusson (6) 4.1 Experments Settng We use Unversty text corpus (Verson 3.0) consstng of artcles of Manch Newspaper. The sentences from the artcles on January 1st, 3rd to 9th are used for the tranng data, and the sentences from the artcles on January 10th are used for the test data. Our experments are under the condton d = 3 (dmenson of the polynomal functons used for the Kernel functon). The features used n the dependency parsng process are shown n Table 1. The features nclude statc features and the dynamc features 4.2 Expermental Results Based on mproved SVM We tran an ntal classfer wth the sentences from artcles on January 1st. Then we prune the data on January 1st, 3rd to 9 th wth LSVM ( ε = 0. 9, experments show that the parsng accuracy s best when ε = 0. 9 ). We prune the tranng data contnue wth NN-SVM and obtaned the fnal classfer. Test the data on January 10th based on SVM, LSVM and NN-LSVM. The expermental results are shown n table 2. Table 2 shows the dependency accuracy and the sentence accuracy are mproved usng large tranng data set based on SVM(86.86% 89.29%, 41.50% 47.53%). The tme requred for tranng and parsng are sgnfcantly ncreased(4mnutes 908mnutes, 0.26sec./sentence 1.7sec./sentence). Table 1. Features used n the dependency parsng process Statc Features left/rght segments Head Word (surface-form, POS, POS-subcategory, nflecton-type, nflecton-form), Functonal Word (surface-form, POS, POS-subcategory, nflecton-type, nflecton-form), brackets, quotaton-marks, punctuaton-marks, poston n sentence Dynamc Features Between two segments The segments whch modfy the current canddate modfee or modfer The segment whch s modfed by the current canddate modfee (begnnng, end) Dstance (1,2-5,6-), case-partcles, brackets, quotaton-marks, punctuaton-marks Form of nflecton represented wth Functonal Representaton POS and POS-subcategory of Head word

5 Proceedngs of the 7th WSEAS Internatonal Conference on Smulaton, Modellng and Optmzaton, Bejng, Chna, September 15-17, We use LSVM and NN-LSVM to prune the tranng data. Even though the number of examples used for LSVM and NN-LSVM s less than that for SVM, dependency accuracy and sentence accuracy are mproved(89.296% 89.86%, 47.53% 49.14%). And the tme requred for tranng and parsng are evdently reduced((908mnutes 129mnutes, 1.7sec./sentence 0.8sec./sentence). Experments show that the mproved SVM not only reduces the tranng cost greatly but also obtans a classfer that has better accuracy and effcency than the classfer obtaned by tranng large set drectly. Tranng wth LSVM ncreases the tranng cost of adjustng the threshold. However the ncreased cost s tme cost nstead of memory cost. Ths cost ncreases lnearly. SVM requres O(n2) tranng cost, (where n s the number of examples.). It ncludes tme cost and memory cost, and ncreases wth the square of the number of examples. Model Tranng data (days) Dependency Acc. (%) Sentence Acc. (%) Parsng Tme (sec./ sentence) The number of support vectors The number of examples Tranng Tme (mnutes) Table 2. Results based on NN-LSVM SVM SVM LSVM NN- LSVM Combned mproved SVM wth KNN Table 3 shows that the approach combned mprove SVM and KNN acheved hgher dependency accuracy and sentence accuracy than the mproved SVM when k s between 27 and 33 n the confdence ε =0.1. However, the approach combned mproved SVM and KNN cannot performance better than the mproved SVM whenε Ths means that the msclassfed samples are manly near the hyperplane. Table 3. Results based on mproved SVM and KNN Dep. Sen. Dep. Sen. Acc. (%) Acc. (%) Acc. (%) Acc. (%) (ε =0.1) (ε =0.1) (ε =0.2) (ε =0.2) K= K= K= K= K= K= K= K= K= K= Comparson wth Related Work The results of our model and the recent Japanese Dependency Analyss model (cascaded chunkng [3], ME [1], ME + posteror context [2]) are summarzed n table 4. Dependency accuracy and sentence accuracy are mproved usng mproved SVM and KNN. The tme requred for tranng and parsng cannot be compared snce the computers used for each experment are dfferent. Table 4. Comparson wth the related work Model Tranng Corpus (# of days) Dependency Acc. (%) Improved SVM and KNN Improved SVM Cascaded chunkng[4] Probablstc (ME)[1] Probablstc (ME + posteror context)[2] Unv. (8) Unv. (8) Unv. (8) Unv. (8) Unv. (8) Sentenc e Acc. (%) Concluson Ths paper presented a method for Japanese dependency analyss that usng mproved SVM and KNN. The mproved SVM delete those samples that are unused or not good to mprove the classfer s performance, and then traned wth the reducton set to obtan the fnal classfer. Experments show that the mproved SVM not only reduces the tranng cost

6 Proceedngs of the 7th WSEAS Internatonal Conference on Smulaton, Modellng and Optmzaton, Bejng, Chna, September 15-17, greatly but also obtans a Japanese analyzer that outperforms the analyzer obtaned by tranng large set drectly wth respect to accuracy and effcency. Furthermore, we combned mproved SVM and KNN. The expermental results show that that the mxed algorthm can mprove the dependency accuracy compared to mproved SVM. References: [1] Kyotaka Uchmoto, Satosh Sekne, Htosh Isahara, Japanese Dependency Structure Analyss Based on Maxmum Entropy Models. In Proceedngs of the EACL, 1999, pp [2] Kyotaka Uchmoto, Masak Murata, Satosh Sekne, Htosh Isahara, Dependency Model usng posteror context. In Proceedngs of Sxth Internatonal Workshop on Parsng Technologes, 2000, pp [3] Taku Kuto, Yuj Matsumoto, Japanese Dependency Structure Analyss Based on Support Vector Machnes. In Emprcal Methods n Natural Language processng and Very Large Corpora, 2000, pp [4] Taku Kuto, Yuj Matsumoto, Japanese Dependency Analyss usng Cascaded Chunkng. In Conference on Computatonal Natural Language Learnng, Tape, Tawan, 2002, pp [5] Vapnk, V.N., The Nature of Statstcal Learnng Theory. Sprnger-Verlag, Berln, 1995 [6] LI Hong-Lan, WANG Chun-Hua, YUAN Bao_Zong, ZHU Zhan-Hu, A Learnng Strategy of SVM to Used Large Tranng Set. Chnese Journal of Computer, Vol. 27, 2004, pp [7] LI Hong-Lan, WANG Chun-Hua, YUAN Bao-Zong: An Improved SVM, NN-SVM. Chnese Journal of Computer, Vol. 26, 2003, pp [8] LI Rong, YE Sh-we, and SHI Zhong-zh, SVM-KNN Classfer-a New Method of Improvng the Accurracy of SVM Classfer, Acta Electronca Snca, vol. 30, 2002, pp

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