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1 % tri-gram 92.76% 98.49% : Japanese word segmentation by Adaboost using the decision list as the weak learner Hiroyuki Shinnou In this paper, we propose the new method of Japanese word segmentation by Adaboost using the decision list as the weak learner. The word segmentation is regarded as the classification problem of judging whether the word boundary exists between two characters or not. By solving the problem by the decision list method, we can conduct Japanese word segmentation. Our method has the advantage not to suffer the unknown word problem because we do not use dictionary information as an attribute of our decision list. Moreover, by taking this approach we can use Adaboost which is actively researched in the machine learning domain recently. Adaboost improves the precision of our decision list. In experiments, we built the decision list through Kyoto University Corpus (about 40K sentences). The precision of this decision list was 97.52%. This values was much higher than the precision of character based tri-gram model, 92.76%. By using Adaboost method, our precision was improved to 98.49%. Furthermore, our word segmentation system was excellent in detecting unknown words. KeyWords: Word segmentation, classification problem, decision list, Adaboost, Faculty of Engineering, Ibaraki University Department of Systems Engineering 3
2 Vol. 8 No. 2 Apr HMM (Hidden Markov Model) HMM HMM a b c tri-gram 2 HMM ( 1997; Tsuji and Kageura 1997; 1998) HMM +1 1 n-gram (Yarowsky 1994) (Freund and Schapire 1997) 4
3 tri-gram HMM n s = c 1 c 2 c n c i c i c i+1 b i (+1) ( 1) b i i = 1, 2,, n %wwâwwww ww*ww]ww_wwtwwjww ww ww wwuww)ww<ww"wwï ˆ ˆ ˆ ˆ ˆ ˆ ˆ ˆ ˆ ˆ ˆ ˆ ˆ ˆ ˆ ˆ %wwâw w w *w ]ww_wwtwwjww ww ww w Uw )ww<ww"w ï 1 1 Word segmentation by class assignment 2.2 5
4 Vol. 8 No. 2 Apr step 1 step 2 step 3 n att 1, att 2,, att n att a C (att, a) C ((att, a), C) 1 ((att, a), C) f C f C Ĉ (att, a) pw(att, a) pw((att, a)) = log fĉ C Ĉ f C step b i b i Setting attributes att 1 c i 1c ic i+1 att 2 c ic i+1c i+2 att 3 c i 1c i att 4 c ic i+1 att 5 c i+1c i+2 att 6 1 ((c i ), (c i+1 )) att 7 2 ((c i ), (c i+1 ))
5 2 2 Classification of character types default default default default b b 5 7 (att 1, ) (att 2, ) (att 3, ) (att 4, ) (att 5, ) (att 6, ) (att 7, ) 7
6 Vol. 8 No. 2 Apr Example of class judgement (att 1, ) (att 2, ) (att 3, ) (att 4, ) (att 5, ) (att 6, ) (att 7, ) (att 7, ) +1 b ( 2 Y ) {+1, 1} 2 (x 1, y 1 ), (x 2, y 2 ),, (x m, y m ) x i y i x i y i +1 1 h 1 h 1 h 1 x i x i y i h 1 x i (x 1, y 1 ), (x 2, y 2 ),, m, y m ) h 2 T T h 1, h 2,, h T T = 3 x h 1 +1 h 2 8
7 1 h ǫ t ) Given: (x 1, y 1 ),, (x m, y m ) where x i X,y i Y = {1, 1} Initialize Di(i) = 1/m For t = 1,, T Train weak learner using distribution D t Get weak hypothesis h t : X Y with error ǫ t = Pr i Dt [h t (x i ) y i ] Choose α t = 1 2 ln(1 ǫ t ǫ t ) Update: { D t+1 (i) = D t(i) Z t e αt if h t (x i ) = y i e αt if h t (x i ) y i where Z t is a normalization factor Output the final hypothesis: T H(x) = sign( α t h t (x)) t=1 2 2 AdaBoost / 9
8 Vol. 8 No. 2 Apr / / / / 4 5 b 4 (att 1, ) (att 2, ) (att 3, ) (att 4, ) (att 5, ) (att 6, ) (att 7, ) 1 step 2 1 ((att 1, ), 1) ((att 2, ), 1) ((att 3, ), 1) ((att 4, ), 1) ((att 5, ), 1) ((att 6, ), 1) ((att 7, ), 1) 1 h k h k+1 1 step n-gram n-gram ( 1998) n-gram n-gram Viterbi HMM ( 4 ) 10
9 KNP 1, ,717 1,234 56,411 56,411 tri-gram CMU-Cambridge Toolkit 2 Witten-Bell discounting 0 ( 1999) tri-gram tri-gram 56,411 52,328 4, % 7 136,114 56,411 55,015 1, % tri-gram 92.76% 4.2 % ,411 55, % ,717 1,234 1 EOS 2 CMU-Cambridge Toolkit prc14/toolkit.html 11
10 Vol. 8 No. 2 Apr precision Precision by boosting 914,392 41,890 32,764 6,479 1, , , % 67.5% 9 (1)
11 (2) (1) (3) (4) 7 3 (5) (6) (7) (8) (9) (7) (8) 13
12 Vol. 8 No. 2 Apr Detection of unknown words (1) (2) (3) (4) (5) (6) (7) (8) (9) , (67.2%) 406 (39.6%) % 67.2% (1),(3),(4),(5),(7) 83.3% , % (Quinlan 1993) (Ratnaparkhi 1998) 7 bi-gram tri-gram
13 Identification strength and precision bi-gram tri-gram 50% 15
14 Vol. 8 No. 2 Apr (,, 1999) 2 3 ( 1998; 1999) ( 2000) (Shinnou 2000) 6 n-gram K11 IV
15 Freund, Y. and Schapire, R. E. (1997). A decision-theoretic generalization of on-line learning and an application to boosting. Journal of Computer and System Sciences, 55 (1), Quinlan, J. R. (1993). C4.5: Programs for Machine Learning. Morgan Kaufmann Publisher. Ratnaparkhi, A. (1998). Maximum Entropy Models for Natural Language Ambiguity Resolution. In PhD thesis. University of Pennsylvania. Shinnou, H. (2000). Deterministic Japanese Word Segmentation by Decision List Method. In PRICAI-2000 (poster session), pp Tsuji, K. and Kageura, K. (1997). An HMM-based Method for Segmenting Japanese Terms and Keywords based on Domain-Specific Bilingual Corpora. In The 4th Natural Language Processing Pacific Rim Symposium, pp Yarowsky, D. (1994). Decision Lists for Lexical Ambiguity Resolution: Application to Accent Restoration in Spanish and French. In 32th Annual Meeting of the Association for Computational Linguistics, pp (1999). Suffix array., NL (1998). PPM., NL ,, (1999).., 6 (7), (1999)... (2000).., NL (1998).. 4, pp (1997).. 3, pp
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