Vol. 23 No. 5 December (Rule-Based Machine Translation; RBMT (Nirenburg 1989)) 2 (Statistical Machine Translation; SMT (Brown, Pietra, Piet

Size: px
Start display at page:

Download "Vol. 23 No. 5 December (Rule-Based Machine Translation; RBMT (Nirenburg 1989)) 2 (Statistical Machine Translation; SMT (Brown, Pietra, Piet"

Transcription

1 Graham Neubig, Sakriani Sakti 2,,,,, Improving Pivot Translation by Remembering the Pivot Akiva Miura, Graham Neubig,, Sakriani Sakti, Tomoki Toda and Satoshi Nakamura In statistical machine translation, the pivot translation approach allows for translation of language pairs with little or no parallel data by introducing a third language for which data exists. In particular, the triangulation method, which translates by combining source-pivot and pivot-target translation models into a source-target model is known for its high translation accuracy. However, in the conventional triangulation method, information of pivot phrases is forgotten, and not used in the translation process. In this research, we propose a novel approach to remember the pivot phrases in the triangulation stage, and use a pivot language model as an additional information source at translation phase. Experimental results on the united nations parallel corpus showed significant improvements in all tested combinations of languages. Key Words: Statistical Machine Translation, Multilinguality, Pivot Translation, Synchronous Context-Free Grammars, Language Models, Parallel Corpora, Graduate School of Information Science, Nara Institute of Science and Technology, Language Technologies Institute, Carnegie Mellon University, Information Technology Center, Nagoya University

2 Vol. 23 No. 5 December (Rule-Based Machine Translation; RBMT (Nirenburg 1989)) 2 (Statistical Machine Translation; SMT (Brown, Pietra, Pietra, and Mercer 1993)) 2 SMT (Dyer, Cordova, Mont, and Lin 2008) (Pvt) (de Gispert and Mariño 2006; Cohn and Lapata 2007; Zhu, He, Wu, Zhu, Wang, and Zhao 2014) 2 (Cascade Translation (de Gispert and Mariño 2006)) (Src-Pvt) (Pvt-Trg) 2 SMT (Src-Trg) SMT (Triangulation (Cohn and Lapata 2007)) (Utiyama and Isahara 2007) SMT (Phrase-Based Machine Translation; PBMT (Koehn, Och, and Marcu 2003)) 2

3 Neubig Sakti,, PBMT SMT (Synchronous Context-Free Grammar; SCFG (Chiang 2007)) SMT PBMT PBMT SCFG Src-Trg 近似 approach approach approccio アプローチ approximation approximation accesso 接近 access (a) - (Src-Pvt ) access ravvicinamento (b) - (Pvt-Trg ) 近似 approccio 近似 approccio アプローチ accesso アプローチ accesso 接近 ravvicinamento 接近 ravvicinamento (c) - (d) Src-Pvt Pvt-Trg Src-Trg 1 (a) (b) (c) (d) 1(c) Src-Trg 3

4 Vol. 23 No. 5 December PBMT SMT 1.1 PBMT SMT SCFG SCFG PBMT 1 2 1(c) 1.1 SMT ( ) SMT ( ) 1 2 SMT (2.1 ) SMT (PBMT, 2.2 ) (SCFG, 2.3 ) SCFG 3 1 ( Neubig Sakti 2014, 2015) ACL 2015: The 53rd Annual Meeting of the Association for Computational Linguistics (Miura, Neubig, Sakti, Toda, and Nakamura 2015) 4

5 Neubig Sakti,, (Multi-Synchronous Context-Free Grammar; MSCFG, 2.4 ) 2.1 SMT (Shannon 1948) f E(f) f e E(f) P r(e f) e SMT P r(e f) ê E(f) ê = arg max P r(e f) (1) e E(f) = arg max e E(f) P r(f e)p r(e) P r(f) = arg max P r(f e)p (e) (3) e E(f) (Och 2003) ê = arg max P r(e f) (4) e E(f) arg max e E(f) exp ( w T h(f, e ) e exp (w T h(f, e )) = arg max w T h(f, e) (6) e E(f) h P r(e) P r(f e) SMT w h w ( ) BLEU(Papineni, Roukos, Ward, and Zhu 2002) (Och 2003) 2.2 (2) (5) 5

6 Vol. 23 No. 5 December Koehn (PBMT (Koehn et al. 2003)) SMT PBMT (Brown et al. 1993) John hit a ball. John hit a ball. ジョンはボールを打った ジョンはボールを打った PBMT PBMT (6) PBMT 2 (Goto, Utiyama, Sumita, Tamura, and Kurohashi 2013) 2 6

7 Neubig Sakti,, 2.3 SMT (SCFG (Chiang 2007)) SCFG (Hierarchical Phrase-Based Translation; Hiero (Chiang 2007)) SCFG X s, t (7) X s t s t X X 0 of X 1, X 1 X 0 (8) Hiero SCFG PBMT X i SCFG 2 X X 0 hit X 1., X 0 X 1 (9) X John, (10) X a ball, (11) S S X 0, X 0 S = X 0, X 0 (12) = X 1 hit X 2., X 1 X 2 (13) = John hit X 2., X 2 (14) = John hit a ball., (15) SCFG ϕ(s t) ϕ(t s) ϕ lex (s t) ϕ lex (t s) (t ) ( 1) 6 7

8 Vol. 23 No. 5 December 2016 CKY+ (Chappelier, Rajman, et al. 1998) (Chiang 2007) 2.4 SCFG (MSCFG (Neubig, Arthur, and Duh 2015)) SCFG t MSCFG N X s, t 1,, t N (16) MSCFG SCFG 3 1 N 1 SCFG PBMT MSCFG 3 3 PBMT MSCFG 8

9 Neubig Sakti,, 3 2 SMT SMT SMT 100 SMT PBMT 4 PBMT SCFG Src Trg Pvt Src-Pvt, Src-Trg, Pvt-Trg 3.1 対訳 Src-Pvt 対訳 Pvt-Trg Src SMT SMT Src Pvt Pvt Pvt Trg Trg 4 (Cascade) (de Gispert and Mariño 2006) Src Trg 4 Src-Pvt, Pvt-Trg Src Pvt Pvt Trg Src Trg 9

10 Vol. 23 No. 5 December 2016 PBMT 2 Src-Pvt n Pvt-Trg (Utiyama and Isahara 2007) n 3.2 対訳 Pvt-Trg 対訳 Src-Pvt SMT Pvt Trg 擬似対訳 Src-Trgʼ Src SMT Src Trg Trg 5 Src-Trg SMT (Synthetic) (de Gispert and Mariño 2006) Src-Trg 5 Src-Pvt, Pvt-Trg Pvt-Trg SMT Src-Pvt Pvt Pvt-Trg Src-Trg Src-Trg SMT SMT De Gispert (de Gispert and Mariño 2006) 10

11 Neubig Sakti,, 対訳 Src-Pvt 対訳 Pvt-Trg SMT Src Pvt SMT Pvt Trg Src SMT Src Trg Trg PBMT SCFG Src-Trg 6 Cohn (Triangulation) (Cohn and Lapata 2007) Src-Pvt Pvt-Trg T SP, T P T T SP, T P T Src-Trg T ST T ST ϕ( ) ϕ lex ( ) ϕ ( t s ) ( ) = t p ϕ (p s) (17) p T SP T P T ϕ ϕ ( s t ) ( ) = ϕ (s p) ϕ p t (18) p T SP T P T ( ) ( ) ϕ lex t s = ϕ lex t p ϕlex (p s) (19) p T SP T P T ( ) ( ) ϕ lex s t = ϕ lex (s p) ϕ lex p t (20) p T SP T P T s, p, t Src, Pvt, Trg p T SP T P T p T SP, T P T (17)-(20) ϕ ( t p, s ) = ϕ ( t p ) (21) ϕ ( s p, t ) = ϕ (s p) (22) 11

12 Vol. 23 No. 5 December 2016 Utiyama (Utiyama and Isahara 2007) n = 1 n = 15 BLEU 4 3 SMT SCFG SMT PBMT SCFG (7) SCFG PBMT PBMT SCFG 4.1 SCFG Src-Pvt, Pvt-Trg (2.3 ) Src-Pvt, Pvt-Trg Pvt X s, p, X p, t X s, t (17)-(20) PBMT s, p, t ( ) SCFG X s, t X p, t PBMT PBMT SCFG 12

13 Neubig Sakti,, (Ziemski, Junczys-Dowmunt, and Pouliquen 2016) (En) (Ar) (Es) (Fr) (Ru) (Zh) 6 1,100 6 SMT 5 Src-Pvt Pvt-Trg Src-Trg Pvt (train) 1,100 (test) 4,000 (dev) 4,000 train 60, test, dev 80 train 800 test, dev 3,800 train Src-Pvt train1 Pvt-Trg train2 10 test dev 1,500 PBMT SCFG SMT Direct ( ): Pvt Src-Trg train1 train2 train1, train2 Direct 1 Direct 2 Direct 1 / 2 Cascade ( ): Src-Pvt, Pvt-Trg train1 train2 Src-Trg Triangulation ( ): Src-Pvt, Pvt-Trg train1, train2 Src-Trg 13

14 Vol. 23 No. 5 December Source Target MT Method Ar Es Fr Ru Zh Es Fr Ru Zh Ar Fr Ru Zh Ar Es Ru Zh Ar Es Fr Zh Ar Es Fr Ru BLEU Score [%] Direct 1 / 2 Triangulation Cascade PBMT / Hiero / PBMT / Hiero / PBMT / Hiero / PBMT / Hiero / PBMT / Hiero / PBMT / Hiero / PBMT / Hiero / PBMT / Hiero / PBMT / Hiero / PBMT / Hiero / PBMT / Hiero / PBMT / Hiero / PBMT / Hiero / PBMT / Hiero / PBMT / Hiero / PBMT / Hiero / PBMT 8.93 / Hiero / PBMT / Hiero / PBMT / Hiero / PBMT / Hiero /

15 Neubig Sakti,, KyTea (Neubig, Nakata, and Mori 2011) PBMT Moses (Koehn, Hoang, Birch, Callison-Burch, Federico, Bertoldi, Cowan, Shen, Moran, Zens, Dyer, Bojar, Constantin, and Herbst 2007) SCFG Travatar (Neubig 2013) Hiero KenLM (Heafield 2011) train1+train gram BLEU (Papineni et al. 2002) SMT MERT (Och 2003) BLEU 4.3 Direct 1 / 2, Triangulation, Cascade 1 PBMT Triangulation Cascade 4.1 SCFG Triangulation Cascade SMT Triangulation Cascade Triangulation Direct Hiero Direct BLEU Triangulation BLEU Direct Triangulation Direct BLEU 15 Triangulation Hiero Triangulation Direct PBMT Hiero Hiero PBMT Hiero Triangulation PBMT 0.5 BLEU Hiero 15

16 Vol. 23 No. 5 December ,,. Europarl (Koehn 2005) Europarl 10 1,500 Src-Pvt Pvt-Trg 4.3 PBMT Hiero Triangulation Cascade 2 Triangulation Direct PBMT Hiero PBMT PBMT SCFG SCFG PBMT PBMT (Utiyama and Isahara 2007) 1 (Zhu et al. 2014) (Levinboim and Chiang 2015) 16

17 Neubig Sakti,, (Dabre, Cromieres, Kurohashi, and Bhattacharyya 2015), 5 3 SMT 4 SCFG SCFG PBMT Src-Trg Src-Pvt, Pvt-Trg Src-Trg Src-Trg 近似 アプローチ 接近 approach approximation access approccio accesso ravvicinamento 7 ( - - ) 7 3 approach Src-Trg 8 Src Trg Pvt 17

18 Vol. 23 No. 5 December 2016 近似 (via: approach) approccio 近似 (via: approach) accesso 近似 ravvicinamento (via: approach, approximation) アプローチ (via: approach) approccio 8 Src-Trg Pvt 5.2 近似 近似 近似 アプローチ approccio, approach ravvicinamento, approach ravvicinamento, approximation approccio, approach 9 Src Trg Pvt Src Trg Pvt 9 SCFG (2.3 ) MSCFG (2.4 ) MSCFG Src-Pvt, Pvt-Trg SCFG 18

19 Neubig Sakti,, SCFG Src-Trg-Pvt MSCFG Pvt SCFG MSCFG SCFG Src-Pvt, Pvt-Trg Pvt X s, p, X p, t X s, t (17)-(20) X s, p, X p, t X s, t, p (23) (17)-(20) Trg Pvt ϕ ( t, p s ) ϕ ( s p, t ) ϕ ( t, p s ) = ϕ ( t p ) ϕ (p s) (24) ϕ ( s p, t ) = ϕ (s p) (25) Src-Pvt ϕ (p s) ϕ (s p) ϕ lex (p s) ϕ lex (s p) T SP 10 ϕ ( t s ) ϕ ( s t ) ϕ (p s) ϕ (s p) ( ) ( ) ϕ lex t s ϕlex s t ϕlex (p s) ϕ lex (s p) ϕ ( t, p s ) ϕ ( s p, t ) t p MSCFG 5.4 s, t s, t, p Neubig T 1 T 2 T 1 - (Neubig et al. 2015) T 1 = T rg T 2 = P vt s T rg ϕ ( t s ) L t t ϕ ( t, p s ) 19

20 Vol. 23 No. 5 December 2016 p (En) (Ar) (Es) (Fr) (Ru) (Zh) 5 Src-Pvt (train1) 10 Pvt-Trg (train2) 10 (test) (dev) 1, train1+train SCFG MSCFG Travatar (Neubig 2013) Hiero SCFG BLEU (Papineni et al. 2002) MERT (Och 2003) BLEU MSCFG L = 20 T 1-6 Cascade ( ): Src-Pvt Pvt-Trg SCFG (3.1 ) w/ PvtLM 200k/5M Src-Pvt Tri. SCFG (SCFG ): Src-Pvt Pvt-Trg SCFG Src-Trg SCFG (3.3 ) Tri. MSCFG (MSCFG ): Src-Pvt Pvt-Trg SCFG Src-Trg-Pvt MSCFG (5 ) w/o PvtLM w/ PvtLM 200k/5M 20

21 Neubig Sakti,, 2 Src Ar Es Fr Ru Zh BLEU Score [%] Trg Cascade Cascade Tri. SCFG Tri. MSCFG Tri. MSCFG Tri. MSCFG w/ PvtLM 200k w/ PvtLM 5M (baseline) w/o PvtLM w/ PvtLM 200k w/ PvtLM 5M Es Fr Ru Zh Ar Fr Ru Zh Ar Es Ru Zh Ar Es Fr Zh Ar Es Fr Ru (Koehn 2004). Tri. SCFG BLEU Tri. SCFG ( : p < 0.05, : p < 0.01) BLEU BLEU MSCFG 21

22 Vol. 23 No. 5 December 2016 BLEU Score [%] 25.0 Translation Accuracy vs. Pivot-LM Size (Zh-Es via En) Direct 1 Tri. SCFG Tri. MSCFG 24.8 Direct Pivot-LM Size [Sent.] BLEU Score [%] 17.6 Translation Accuracy vs. Pivot-LM Size (Ar-Ru via En) Direct 1 Tri. SCFG Tri. MSCFG 17.4 Direct Pivot-LM Size [Sent.] 10 (Tri. MSCFG w/o PvtLM) SCFG,,,500 (Cascade w/ PvtLM 5M). Cascade w/ PvtLM 5M 20 (Cascade w/ PvtLM 200k) Zh-Ar Zh-Ru, Tri. SCFG,., Tri. MSCFG w/ PvtLM 5M,Cascade w/ PvtLM 5M,, ( ) ( ) 22

23 Neubig Sakti,, (Brants, Popat, Xu, Och, and Dean 2007) ( ): Le nom du candidat proposé est indiqué dans l annexa à la présente note. ( ): El nombre del candidato propuesto se presenta en el anexo de la presente nota. : The name of the candidate thus nominated is set out in the annex to the present note. Tri. SCFG: El nombre del proyecto de un candidato se indica en el anexo a la presente nota. (BLEU+1: 34.99) Tri. MSCFG w/ PvtLM 5M: El nombre del candidato propuesto se indica en el anexo a la presente nota. (BLEU+1: 61.13) The name of the candidate proposed indicated in the annex to the present note. ( ) proposé ( ) propuesto proyecto ( ) proposé propuesto proposed ( ): J. Risques d aspiration : citère de viscosité pour la classification des mélanges ; ( ): J. Peligros por aspiración : criterio de viscosidad para la clasificación de mezclas ; 23

24 Vol. 23 No. 5 December Direct F-Measure [%] Tri. MSCFG w/ PvtLM 2M Tri.SCFG NC ( ) 8, (+0.01) P ( ) 6, (+1.02) DET ( ) 5, (+1.50) PUNC ( ) 4, (+0.44) ADJ ( ) 3, (+0.67) V ( ) 3, (+1.06) ADV ( ) 2, (+0.34) : J. Aspiration hazards : viscosity criterion for classification of mixtures ; Direct 1: J. Riesgos d aspiration : criterio de viscosité para la clasificación de los mélanges ; (BLEU+1: 34.20) Direct 2: J. Riesgos d aspiration : criterio de viscosité para la clasificación de mezclas ; (BLEU+1: 49.16) Tri. MSCFG w/ PvtLM 2M: J. Riesgos d aspiration : viscosité criterios para la clasificación de mélanges ; (BLEU+1: 27.61) J. Risk d aspiration : viscosité criteria for the categorization of mélanges ; ( ) d aspiration ( ) mélanges ( ) d aspiration train1 train2 Direct 1 / 2 mélanges train2 Direct 1 2 criterio ( ) criterios 24

25 Neubig Sakti,, 4 Direct F-Measure [%] Tri. MSCFG w/ PvtLM 2M Tri.SCFG NN ( ) 10, (+0.53) ART ( ) 4, (+1.24) CARD ( ) 3, ( 0.44) APPR ( ) 2, (+1.50) ADJA ( ) 2, (+0.47) NE ( ) 2, (+0.47) ADV ( ) 1, (+0.18) PPEF ( ) 1, (+1.67) Europarl 4.4 Europarl Tri. SCFG Cascade Stanford POS Tagger (Toutanova and Manning 2000; Toutanova, Klein, Manning, and Singer 2003) F 3 4 F 3 F F 25

26 Vol. 23 No. 5 December 2016 Direct F Direct Direct F 4 3 F Direct 7 PBMT SCFG 6 26

27 Neubig Sakti,, 1 SCFG 2 JSPS 16H ATR-Trek 27

28 Vol. 23 No. 5 December 2016 Brants, T., Popat, A. C., Xu, P., Och, F. J., and Dean, J. (2007). Large Language Models in Machine Translation. In Proc. EMNLP, pp Brown, P. F., Pietra, V. J., Pietra, S. A. D., and Mercer, R. L. (1993). The Mathematics of Statistical Machine Translation: Parameter Estimation. Computational Linguistics, 19, pp Chappelier, J.-C., Rajman, M., et al. (1998). A Generalized CYK Algorithm for Parsing Stochastic CFG. In Proc. TAPD, Vol. 98, p. 5. Citeseer. Chiang, D. (2007). Hierarchical phrase-based translation. Computational Linguistics, 33 (2), pp Cohn, T. and Lapata, M. (2007). Machine Translation by Triangulation: Making Effective Use of Multi-Parallel Corpora. In Proc. ACL, pp Dabre, R., Cromieres, F., Kurohashi, S., and Bhattacharyya, P. (2015). Leveraging Small Multilingual Corpora for SMT Using Many Pivot Languages. In Proc. NAACL, pp de Gispert, A. and Mariño, J. B. (2006). Catalan-English Statistical Machine Translation without Parallel Corpus: Bridging through Spanish. In Proc. of LREC 5th Workshop on Strategies for developing machine translation for minority languages. Dyer, C., Cordova, A., Mont, A., and Lin, J. (2008). Fast, Easy, and Cheap: Construction of Statistical Machine Translation Models with MapReduce. In Proc. WMT, pp Goto, I., Utiyama, M., Sumita, E., Tamura, A., and Kurohashi, S. (2013). Distortion Model Considering Rich Context for Statistical Machine Translation. In Proc. ACL, pp Heafield, K. (2011). KenLM: Faster and Smaller Language Model Queries. In Proc, WMT. Koehn, P. (2004). Statistical Significance Tests for Machine Translation Evaluation. In Lin, D. and Wu, D. (Eds.), Proc. EMNLP, pp Koehn, P. (2005). Europarl: A parallel corpus for statistical machine translation. In MT summit, Vol. 5, pp Koehn, P., Hoang, H., Birch, A., Callison-Burch, C., Federico, M., Bertoldi, N., Cowan, B., Shen, W., Moran, C., Zens, R., Dyer, C., Bojar, O., Constantin, A., and Herbst, E. (2007). Moses: Open Source Toolkit for Statistical Machine Translation. In Proc. ACL, pp Koehn, P., Och, F. J., and Marcu, D. (2003). Statistical Phrase-Based Translation. In Proc. NAACL, pp Levinboim, T. and Chiang, D. (2015). Supervised Phrase Table Triangulation with Neural Word Embeddings for Low-Resource Languages. In Proc. EMNLP, pp

29 Neubig Sakti,, Miura, A., Neubig, G., Sakti, S., Toda, T., and Nakamura, S. (2015). Improving Pivot Translation by Remembering the Pivot. In Proc. ACL, pp Neubig, G. (2013). Travatar: A Forest-to-String Machine Translation Engine based on Tree Transducers. In Proc. ACL Demo Track, pp Neubig, G., Arthur, P., and Duh, K. (2015). Multi-Target Machine Translation with Multi- Synchronous Context-free Grammars. In Proc. NAACL, pp Neubig, G., Nakata, Y., and Mori, S. (2011). Pointwise Prediction for Robust, Adaptable Japanese Morphological Analysis. In Proc. ACL, pp Nirenburg, S. (1989). Knowledge-Based Machine Translation. Machine Translation, 4 (1), pp Och, F. J. (2003). Minimum Error Rate Training in Statistical Machine Translation. In Proc. ACL, pp Papineni, K., Roukos, S., Ward, T., and Zhu, W.-J. (2002). BLEU: a Method for Automatic Evaluation of Machine Translation. In Proc. ACL, pp Shannon, C. E. (1948). A Mathematical Theory of Communication. Bell System Technical Journal, 27 (3), pp Toutanova, K., Klein, D., Manning, C. D., and Singer, Y. (2003). Feature-rich Part-of-speech Tagging with a Cyclic Dependency Network. In Proc. NAACL, pp Toutanova, K. and Manning, C. D. (2000). Enriching the Knowledge Sources Used in a Maximum Entropy Part-of-Speech Tagger. In Proc. EMNLP, pp Utiyama, M. and Isahara, H. (2007). A Comparison of Pivot Methods for Phrase-Based Statistical Machine Translation. In Proc. NAACL, pp Zhu, X., He, Z., Wu, H., Zhu, C., Wang, H., and Zhao, T. (2014). Improving Pivot-Based Statistical Machine Translation by Pivoting the Co-occurrence Count of Phrase Pairs. In Proc. EMNLP, pp Ziemski, M., Junczys-Dowmunt, M., and Pouliquen, B. (2016). The United Nations Parallel Corpus v1.0. In Proc. LREC, pp Neubig Graham Sakti Sakriani (2014) (SIG-NL). Neubig Graham Sakti Sakriani (2015) (SIG-NL)

30 Vol. 23 No. 5 December ACL Graham Neubig: Sakriani Sakti: ATR INRIA JNS SFN ASJ ISCA IEICE IEEE PD IEEE ATR 2006 ( ) ATR Antonio Zampoli ISCA IEEE SLTC IEEE 30

Vol. 23 No. 5 December (Rule-Based Machine Translation; RBMT (Nirenburg 1989)) 2 (Statistical Machine Translation; SMT (Brown, Pietra, Piet

Vol. 23 No. 5 December (Rule-Based Machine Translation; RBMT (Nirenburg 1989)) 2 (Statistical Machine Translation; SMT (Brown, Pietra, Piet Graham Neubig, Sakriani Sakti 2 Improving Pivot Translation by Remembering the Pivot Akiva Miura, Graham Neubig,, Sakriani Sakti, Tomoki Toda and Satoshi Nakamura In statistical machine translation, the

More information

f ê ê = arg max Pr(e f) (1) e M = arg max λ m h m (e, f) (2) e m=1 h m (e, f) λ m λ m BLEU [11] [12] PBMT 2 [13][14] 2.2 PBMT Hiero[9] Chiang PBMT [X

f ê ê = arg max Pr(e f) (1) e M = arg max λ m h m (e, f) (2) e m=1 h m (e, f) λ m λ m BLEU [11] [12] PBMT 2 [13][14] 2.2 PBMT Hiero[9] Chiang PBMT [X 1,a) Graham Neubig 1,b) Sakriani Sakti 1,c) 1,d) 1,e) 1. Statistical Machine Translation: SMT[1] [2] [3][4][5][6] 2 Cascade Translation [3] Triangulation [7] Phrase-Based Machine Translation: PBMT[8] 1

More information

IPSJ SIG Technical Report Vol.2014-NL-219 No /12/17 1,a) Graham Neubig 1,b) Sakriani Sakti 1,c) 1,d) 1,e) 1. [23] 1(a) 1(b) [19] n-best [1] 1 N

IPSJ SIG Technical Report Vol.2014-NL-219 No /12/17 1,a) Graham Neubig 1,b) Sakriani Sakti 1,c) 1,d) 1,e) 1. [23] 1(a) 1(b) [19] n-best [1] 1 N 1,a) Graham Neubig 1,b) Sakriani Sakti 1,c) 1,d) 1,e) 1. [23] 1(a) 1(b) [19] n-best [1] 1 Nara Institute of Science and Technology a) akabe.koichi.zx8@is.naist.jp b) neubig@is.naist.jp c) ssakti@is.naist.jp

More information

2

2 NTT 2012 NTT Corporation. All rights reserved. 2 3 4 5 Noisy Channel f : (source), e : (target) ê = argmax e p(e f) = argmax e p(f e)p(e) 6 p( f e) (Brown+ 1990) f1 f2 f3 f4 f5 f6 f7 He is a high school

More information

A Japanese Word Dependency Corpus ÆüËܸì¤Îñ¸ì·¸¤ê¼õ¤±¥³¡¼¥Ñ¥¹

A Japanese Word Dependency Corpus   ÆüËܸì¤Îñ¸ì·¸¤ê¼õ¤±¥³¡¼¥Ñ¥¹ A Japanese Word Dependency Corpus 2015 3 18 Special thanks to NTT CS, 1 /27 Bunsetsu? What is it? ( ) Cf. CoNLL Multilingual Dependency Parsing [Buchholz+ 2006] (, Penn Treebank [Marcus 93]) 2 /27 1. 2.

More information

2014/1 Vol. J97 D No. 1 2 [2] [3] 1 (a) paper (a) (b) (c) 1 Fig. 1 Issues in coordinating translation services. (b) feast feast feast (c) Kran

2014/1 Vol. J97 D No. 1 2 [2] [3] 1 (a) paper (a) (b) (c) 1 Fig. 1 Issues in coordinating translation services. (b) feast feast feast (c) Kran a) b) c) Improving Quality of Pivot Translation by Context in Service Coordination Yohei MURAKAMI a), Rie TANAKA b),andtoruishida c) Web 1. Web 26.8% 30.9% 21.3% 21% 1 n n(n 1) Department of Social Informatics,

More information

¥ì¥·¥Ô¤Î¸À¸ì½èÍý¤Î¸½¾õ

¥ì¥·¥Ô¤Î¸À¸ì½èÍý¤Î¸½¾õ 2013 8 18 Table of Contents = + 1. 2. 3. 4. 5. etc. 1. ( + + ( )) 2. :,,,,,, (MUC 1 ) 3. 4. (subj: person, i-obj: org. ) 1 Message Understanding Conference ( ) UGC 2 ( ) : : 2 User-Generated Content [

More information

21 Pitman-Yor Pitman- Yor [7] n -gram W w n-gram G Pitman-Yor P Y (d, θ, G 0 ) (1) G P Y (d, θ, G 0 ) (1) Pitman-Yor d, θ, G 0 d 0 d 1 θ Pitman-Yor G

21 Pitman-Yor Pitman- Yor [7] n -gram W w n-gram G Pitman-Yor P Y (d, θ, G 0 ) (1) G P Y (d, θ, G 0 ) (1) Pitman-Yor d, θ, G 0 d 0 d 1 θ Pitman-Yor G ol2013-nl-214 No6 1,a) 2,b) n-gram 1 M [1] (TG: Tree ubstitution Grammar) [2], [3] TG TG 1 2 a) ohno@ilabdoshishaacjp b) khatano@maildoshishaacjp [4], [5] [6] 2 Pitman-Yor 3 Pitman-Yor 1 21 Pitman-Yor

More information

( ) Kevin Duh

( ) Kevin Duh NAIST-IS-MT1251045 Factored Translation Models 2014 2 6 ( ) Kevin Duh Factored Translation Models Factored translation models Factored Translation Models, NAIST-IS-MT1251045, 2014 2 6. i Post-ordering

More information

IPSJ-TOD

IPSJ-TOD Vol. 3 No. 2 91 101 (June 2010) 1 1 1 2 1 TSC2 Automatic Evaluation of Text Summaries by Using Paraphrase Kazuho Hirahara, 1 Hidetsugu Nanba, 1 Toshiyuki Takezawa 1 and Manabu Okumura 2 The evaluation

More information

Vol. 43 No. 7 July 2002 ATR-MATRIX,,, ATR ITL ATR-MATRIX ATR-MATRIX 90% ATR-MATRIX Development and Evaluation of ATR-MATRIX Speech Translation System

Vol. 43 No. 7 July 2002 ATR-MATRIX,,, ATR ITL ATR-MATRIX ATR-MATRIX 90% ATR-MATRIX Development and Evaluation of ATR-MATRIX Speech Translation System Vol. 43 No. 7 July 2002 ATR-MATRIX,,, ATR ITL ATR-MATRIX ATR-MATRIX 90% ATR-MATRIX Development and Evaluation of ATR-MATRIX Speech Translation System Fumiaki Sugaya,,, Toshiyuki Takezawa, Eiichiro Sumita,

More information

yasi10.dvi

yasi10.dvi 2002 50 2 259 278 c 2002 1 2 2002 2 14 2002 6 17 73 PML 1. 1997 1998 Swiss Re 2001 Canabarro et al. 1998 2001 1 : 651 0073 1 5 1 IHD 3 2 110 0015 3 3 3 260 50 2 2002, 2. 1 1 2 10 1 1. 261 1. 3. 3.1 2 1

More information

ズテーブルを 用 いて 対 訳 専 門 用 語 を 獲 得 する 手 法 を 提 案 する 具 体 的 には まず 専 門 用 語 対 訳 辞 書 獲 得 の 情 報 源 として 用 いる 日 中 対 訳 文 対 に 対 して 句 に 基 づく 統 計 的 機 械 翻 訳 モデルを 適 用 すること

ズテーブルを 用 いて 対 訳 専 門 用 語 を 獲 得 する 手 法 を 提 案 する 具 体 的 には まず 専 門 用 語 対 訳 辞 書 獲 得 の 情 報 源 として 用 いる 日 中 対 訳 文 対 に 対 して 句 に 基 づく 統 計 的 機 械 翻 訳 モデルを 適 用 すること 日 中 パテントファミリーを 利 用 した 専 門 用 語 訳 語 推 定 フレーズテーブルおよび 対 訳 文 対 を 利 用 する 方 式 Estimating Translation of Technical Terms utilizing Japanese-Chinese Patent Families : an Approach based on Phrase Translation Tables

More information

gengo.dvi

gengo.dvi 4 97.52% 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

More information

IPSJ SIG Technical Report Vol.2010-NL-199 No /11/ treebank ( ) KWIC /MeCab / Morphological and Dependency Structure Annotated Corp

IPSJ SIG Technical Report Vol.2010-NL-199 No /11/ treebank ( ) KWIC /MeCab / Morphological and Dependency Structure Annotated Corp 1. 1 1 1 2 treebank ( ) KWIC /MeCab / Morphological and Dependency Structure Annotated Corpus Management Tool: ChaKi Yuji Matsumoto, 1 Masayuki Asahara, 1 Masakazu Iwatate 1 and Toshio Morita 2 This paper

More information

1 7.35% 74.0% linefeed point c 200 Information Processing Society of Japan

1 7.35% 74.0% linefeed point c 200 Information Processing Society of Japan 1 2 3 Incremental Linefeed Insertion into Lecture Transcription for Automatic Captioning Masaki Murata, 1 Tomohiro Ohno 2 and Shigeki Matsubara 3 The development of a captioning system that supports the

More information

一般社団法人電子情報通信学会 THE INSTITUTE OF ELECTRONICS, INFORMATION AND COMMUNICATION ENGINEERS THE INSTITUTE OF ELECTRONICS, INFORMATION AND COMMUNICATION ENGIN

一般社団法人電子情報通信学会 THE INSTITUTE OF ELECTRONICS, INFORMATION AND COMMUNICATION ENGINEERS THE INSTITUTE OF ELECTRONICS, INFORMATION AND COMMUNICATION ENGIN 一般社団法人電子情報通信学会 THE INSTITUTE OF ELECTRONICS, INFORMATION AND COMMUNICATION ENGINEERS THE INSTITUTE OF ELECTRONICS, INFORMATION AND COMMUNICATION ENGINEERS 信学技報 IEICE Technical Report SP2019-12(2019-08)

More information

Modal Phrase MP because but 2 IP Inflection Phrase IP as long as if IP 3 VP Verb Phrase VP while before [ MP MP [ IP IP [ VP VP ]]] [ MP [ IP [ VP ]]]

Modal Phrase MP because but 2 IP Inflection Phrase IP as long as if IP 3 VP Verb Phrase VP while before [ MP MP [ IP IP [ VP VP ]]] [ MP [ IP [ VP ]]] 30 4 2016 3 pp.195-209. 2014 N=23 (S)AdvOV (S)OAdvV 2 N=17 (S)OAdvV 2014 3, 2008 Koizumi 1993 3 MP IP VP 1 MP 2006 2002 195 Modal Phrase MP because but 2 IP Inflection Phrase IP as long as if IP 3 VP Verb

More information

自然言語処理22_289

自然言語処理22_289 (RNN) Dyer (Dyer, Clark, Lavie, and Smith 2011) IBM (Brown, Pietra, Pietra, and Mercer 1993) word embedding word embedding RNN (Yang, Liu, Li, Zhou, and Yu 2013) IBM 4 Recurrent Neural Networks for Word

More information

Computational Semantics 1 category specificity Warrington (1975); Warrington & Shallice (1979, 1984) 2 basic level superiority 3 super-ordinate catego

Computational Semantics 1 category specificity Warrington (1975); Warrington & Shallice (1979, 1984) 2 basic level superiority 3 super-ordinate catego Computational Semantics 1 category specificity Warrington (1975); Warrington & Shallice (1979, 1984) 2 basic level superiority 3 super-ordinate category preservation 1 / 13 analogy by vector space Figure

More information

x i 2 x x i i 1 i xi+ 1xi+ 2x i+ 3 健康児に本剤を接種し ( 窓幅 3 n-gram 長の上限 3 の場合 ) 文字 ( 種 )1-gram: -3/ 児 (K) -2/ に (H) -1/ 本 (K) 1/ 剤 (K) 2/ を (H) 3/ 接 (K) 文字 (

x i 2 x x i i 1 i xi+ 1xi+ 2x i+ 3 健康児に本剤を接種し ( 窓幅 3 n-gram 長の上限 3 の場合 ) 文字 ( 種 )1-gram: -3/ 児 (K) -2/ に (H) -1/ 本 (K) 1/ 剤 (K) 2/ を (H) 3/ 接 (K) 文字 ( 1. 2 1 NEUBIG Graham 1 1 1 Improving Part-of-Speech Tagging by Combining Pointwise and Sequence-based Predictors Yosuke NAKATA, 1 Graham NEUBIG, 1 Shinsuke MORI 1 and Tatsuya KAWAHARA 1 This paper proposes

More information

EQUIVALENT TRANSFORMATION TECHNIQUE FOR ISLANDING DETECTION METHODS OF SYNCHRONOUS GENERATOR -REACTIVE POWER PERTURBATION METHODS USING AVR OR SVC- Ju

EQUIVALENT TRANSFORMATION TECHNIQUE FOR ISLANDING DETECTION METHODS OF SYNCHRONOUS GENERATOR -REACTIVE POWER PERTURBATION METHODS USING AVR OR SVC- Ju EQUIVALENT TRANSFORMATION TECHNIQUE FOR ISLANDING DETECTION METHODS OF SYNCHRONOUS GENERATOR -REACTIVE POWER PERTURBATION METHODS USING AVR OR SVC- Jun Motohashi, Member, Takashi Ichinose, Member (Tokyo

More information

BLEU Kishore Papineni, Salim Roukos, Todd Ward and Wei-Jing Zhu. (2002) BLEU: a method for Automatic Evaluation of Machine Translation. ACL. MT ( ) MT

BLEU Kishore Papineni, Salim Roukos, Todd Ward and Wei-Jing Zhu. (2002) BLEU: a method for Automatic Evaluation of Machine Translation. ACL. MT ( ) MT 4. BLEU @NICT mutiyama@nict.go.jp 1 BLEU Kishore Papineni, Salim Roukos, Todd Ward and Wei-Jing Zhu. (2002) BLEU: a method for Automatic Evaluation of Machine Translation. ACL. MT ( ) MT ( ) BLEU 2 BLEU

More information

3807 (3)(2) ,267 1 Fig. 1 Advertisement to the author of a blog. 3 (1) (2) (3) (2) (1) TV 2-0 Adsense (2) Web ) 6) 3

3807 (3)(2) ,267 1 Fig. 1 Advertisement to the author of a blog. 3 (1) (2) (3) (2) (1) TV 2-0 Adsense (2) Web ) 6) 3 Vol. 52 No. 12 3806 3816 (Dec. 2011) 1 1 Discovering Latent Solutions from Expressions of Dissatisfaction in Blogs Toshiyuki Sakai 1 and Ko Fujimura 1 This paper aims to find the techniques or goods that

More information

IPSJ SIG Technical Report Vol.2017-SLP-115 No /2/18 1,a) 1 1,2 Sakriani Sakti [1][2] [3][4] [5][6][7] [8] [9] 1 Nara Institute of Scie

IPSJ SIG Technical Report Vol.2017-SLP-115 No /2/18 1,a) 1 1,2 Sakriani Sakti [1][2] [3][4] [5][6][7] [8] [9] 1 Nara Institute of Scie 1,a) 1 1,2 Sakriani Sakti 1 1 1 1. [1][2] [3][4] [5][6][7] [8] [9] 1 Nara Institute of Science and Technology 2 Japan Science and Technology Agency a) ishikawa.yoko.io5@is.naist.jp 2. 1 Belief-Desire theory

More information

THE INSTITUTE OF ELECTRONICS, INFORMATION AND COMMUNICATION ENGINEERS TECHNICAL REPORT OF IEICE.

THE INSTITUTE OF ELECTRONICS, INFORMATION AND COMMUNICATION ENGINEERS TECHNICAL REPORT OF IEICE. THE INSTITUTE OF ELECTRONICS, INFORMATION AND COMMUNICATION ENGINEERS TECHNICAL REPORT OF IEICE. E-mail: {ytamura,takai,tkato,tm}@vision.kuee.kyoto-u.ac.jp Abstract Current Wave Pattern Analysis for Anomaly

More information

130 Oct Radial Basis Function RBF Efficient Market Hypothesis Fama ) 4) 1 Fig. 1 Utility function. 2 Fig. 2 Value function. (1) (2)

130 Oct Radial Basis Function RBF Efficient Market Hypothesis Fama ) 4) 1 Fig. 1 Utility function. 2 Fig. 2 Value function. (1) (2) Vol. 47 No. SIG 14(TOM 15) Oct. 2006 RBF 2 Effect of Stock Investor Agent According to Framing Effect to Stock Exchange in Artificial Stock Market Zhai Fei, Shen Kan, Yusuke Namikawa and Eisuke Kita Several

More information

..,,,, , ( ) 3.,., 3.,., 500, 233.,, 3,,.,, i

..,,,, , ( ) 3.,., 3.,., 500, 233.,, 3,,.,, i 25 Feature Selection for Prediction of Stock Price Time Series 1140357 2014 2 28 ..,,,,. 2013 1 1 12 31, ( ) 3.,., 3.,., 500, 233.,, 3,,.,, i Abstract Feature Selection for Prediction of Stock Price Time

More information

IBM-Mode1 Q: A: cash money It is fine today 2

IBM-Mode1 Q: A: cash money It is fine today 2 8. IBM Model-1 @NICT mutiyama@nict.go.jp 1 IBM-Mode1 Q: A: cash money It is fine today 2 e f a P (f, a e) â : â = arg max a P (f, a e) â P (f, a e) 3 θ P (f e, θ) θ f d = { f, e } L(θ d) = log f,e d P

More information

46 583/4 2012

46 583/4 2012 4-3 A Transliteration System Based on Bayesian Alignment and its Human Evaluation within a Machine Translation System Andrew Finch and YASUDA Keiji This paper reports on contributions in two areas. Firstly,

More information

The Japanese Journal of Health Psychology, 29(S): (2017)

The Japanese Journal of Health Psychology, 29(S): (2017) Journal of Health Psychology Research 2017, Vol. 29, Special issue, 139 149Journal of Health Psychology Research 2016, J-STAGE Vol. Advance 29, Special publication issue, 139 149 date : 5 December, 2016

More information

DEIM Forum 2009 E

DEIM Forum 2009 E DEIM Forum 2009 E5-3 464-8601 1 606-8501 464 8601 1 E-mail: lifushi@arch.itc.nagoya-u.ac.jp, mayumi@mm.media.kyoto-u.ac.jp, {hirano,kajita,mase}@itc.nagoya-u.ac.jp Abstract Study on a Recipe Recommendation

More information

i

i 21 Fault-Toleranted Authentication Data Distribution Protocol for Autonomous Distributed Networks 1125153 2010 3 2 i Abstract Fault-Toleranted Authentication Data Distribution Protocol for Autonomous Distributed

More information

johnny-paper2nd.dvi

johnny-paper2nd.dvi 13 The Rational Trading by Using Economic Fundamentals AOSHIMA Kentaro 14 2 26 ( ) : : : The Rational Trading by Using Economic Fundamentals AOSHIMA Kentaro abstract: Recently Artificial Markets on which

More information

[1] B =b 1 b n P (S B) S S O = {o 1,2, o 1,3,, o 1,n, o 2,3,, o i,j,, o n 1,n } D = {d 1, d 2,, d n 1 } S = O, D o i,j 1 i

[1] B =b 1 b n P (S B) S S O = {o 1,2, o 1,3,, o 1,n, o 2,3,, o i,j,, o n 1,n } D = {d 1, d 2,, d n 1 } S = O, D o i,j 1 i 1,a) 2,b) 3,c) 1,d) CYK 552 1. 2 ( 1 ) ( 2 ) 1 [1] 2 [2] [3] 1 Graduate School of Information Science, Nagoya University, Japan 2 Information Technology Center, Nagoya University, Japan 3 Information &

More information

A Feasibility Study of Direct-Mapping-Type Parallel Processing Method to Solve Linear Equations in Load Flow Calculations Hiroaki Inayoshi, Non-member

A Feasibility Study of Direct-Mapping-Type Parallel Processing Method to Solve Linear Equations in Load Flow Calculations Hiroaki Inayoshi, Non-member A Feasibility Study of Direct-Mapping-Type Parallel Processing Method to Solve Linear Equations in Load Flow Calculations Hiroaki Inayoshi, Non-member (University of Tsukuba), Yasuharu Ohsawa, Member (Kobe

More information

Vol.54 No (July 2013) [9] [10] [11] [12], [13] 1 Fig. 1 Flowchart of the proposed system. c 2013 Information

Vol.54 No (July 2013) [9] [10] [11] [12], [13] 1 Fig. 1 Flowchart of the proposed system. c 2013 Information Vol.54 No.7 1937 1950 (July 2013) 1,a) 2012 11 1, 2013 4 5 1 Similar Sounds Sentences Generator Based on Morphological Analysis Manner and Low Class Words Masaaki Kanakubo 1,a) Received: November 1, 2012,

More information

Outline ACL 2017 ACL ACL 2017 Chairs/Presidents

Outline ACL 2017 ACL ACL 2017 Chairs/Presidents ACL 2017, 2017/9/7 Outline ACL 2017 ACL ACL 2017 Chairs/Presidents ACL ACL he annual meeting of the Association for Computational Linguistics (Computational Linguistics) (Natural Language Processing) /

More information

1., 1 COOKPAD 2, Web.,,,,,,.,, [1]., 5.,, [2].,,.,.,, 5, [3].,,,.,, [4], 33,.,,.,,.. 2.,, 3.., 4., 5., ,. 1.,,., 2.,. 1,,

1., 1 COOKPAD 2, Web.,,,,,,.,, [1]., 5.,, [2].,,.,.,, 5, [3].,,,.,, [4], 33,.,,.,,.. 2.,, 3.., 4., 5., ,. 1.,,., 2.,. 1,, THE INSTITUTE OF ELECTRONICS, INFORMATION AND COMMUNICATION ENGINEERS TECHNICAL REPORT OF IEICE.,, 464 8601 470 0393 101 464 8601 E-mail: matsunagah@murase.m.is.nagoya-u.ac.jp, {ide,murase,hirayama}@is.nagoya-u.ac.jp,

More information

% 95% 2002, 2004, Dunkel 1986, p.100 1

% 95% 2002, 2004, Dunkel 1986, p.100 1 Blended Learning 要 旨 / Moodle Blended Learning Moodle キーワード:Blended Learning Moodle 1 2008 Moodle e Blended Learning 2009.. 1994 2005 1 2 93% 95% 2002, 2004, 2011 2011 1 Dunkel 1986, p.100 1 Blended Learning

More information

258 5) GPS 1 GPS 6) GPS DP 7) 8) 10) GPS GPS 2 3 4 5 2. 2.1 3 1) GPS Global Positioning System

258 5) GPS 1 GPS 6) GPS DP 7) 8) 10) GPS GPS 2 3 4 5 2. 2.1 3 1) GPS Global Positioning System Vol. 52 No. 1 257 268 (Jan. 2011) 1 2, 1 1 measurement. In this paper, a dynamic road map making system is proposed. The proposition system uses probe-cars which has an in-vehicle camera and a GPS receiver.

More information

1 Web [2] Web [3] [4] [5], [6] [7] [8] S.W. [9] 3. MeetingShelf Web MeetingShelf MeetingShelf (1) (2) (3) (4) (5) Web MeetingShelf

1 Web [2] Web [3] [4] [5], [6] [7] [8] S.W. [9] 3. MeetingShelf Web MeetingShelf MeetingShelf (1) (2) (3) (4) (5) Web MeetingShelf 1,a) 2,b) 4,c) 3,d) 4,e) Web A Review Supporting System for Whiteboard Logging Movies Based on Notes Timeline Taniguchi Yoshihide 1,a) Horiguchi Satoshi 2,b) Inoue Akifumi 4,c) Igaki Hiroshi 3,d) Hoshi

More information

IPSJ SIG Technical Report Vol.2012-MUS-96 No /8/10 MIDI Modeling Performance Indeterminacies for Polyphonic Midi Score Following and

IPSJ SIG Technical Report Vol.2012-MUS-96 No /8/10 MIDI Modeling Performance Indeterminacies for Polyphonic Midi Score Following and MIDI 1 2 3 2 1 Modeling Performance Indeterminacies for Polyphonic Midi Score Following and Its Application to Automatic Accompaniment Nakamura Eita 1 Yamamoto Ryuichi 2 Saito Yasuyuki 3 Sako Shinji 2

More information

Hansen 1 2, Skinner 5, Augustinus 6, Harvey 7 Windle 8 Pels 9 1 Skinner 5 Augustinus 6 Pels 9 NL Harvey ML 11 NL

Hansen 1 2, Skinner 5, Augustinus 6, Harvey 7 Windle 8 Pels 9 1 Skinner 5 Augustinus 6 Pels 9 NL Harvey ML 11 NL HANAOKA, Shinya 1 3 Hansen1, 2 1 2 3 Hansen 2 3 4 5 2 2.1 002 Vol.5 No.4 2003 Winter 3 4 2.2 Hansen 1 2, Skinner 5, Augustinus 6, Harvey 7 Windle 8 Pels 9 1 Skinner 5 Augustinus 6 Pels 9 NL Harvey 10 2.3

More information

Vol. 48 No. 3 Mar PM PM PMBOK PM PM PM PM PM A Proposal and Its Demonstration of Developing System for Project Managers through University-Indus

Vol. 48 No. 3 Mar PM PM PMBOK PM PM PM PM PM A Proposal and Its Demonstration of Developing System for Project Managers through University-Indus Vol. 48 No. 3 Mar. 2007 PM PM PMBOK PM PM PM PM PM A Proposal and Its Demonstration of Developing System for Project Managers through University-Industry Collaboration Yoshiaki Matsuzawa and Hajime Ohiwa

More information

& Vol.5 No (Oct. 2015) TV 1,2,a) , Augmented TV TV AR Augmented Reality 3DCG TV Estimation of TV Screen Position and Ro

& Vol.5 No (Oct. 2015) TV 1,2,a) , Augmented TV TV AR Augmented Reality 3DCG TV Estimation of TV Screen Position and Ro TV 1,2,a) 1 2 2015 1 26, 2015 5 21 Augmented TV TV AR Augmented Reality 3DCG TV Estimation of TV Screen Position and Rotation Using Mobile Device Hiroyuki Kawakita 1,2,a) Toshio Nakagawa 1 Makoto Sato

More information

Table 1. Reluctance equalization design. Fig. 2. Voltage vector of LSynRM. Fig. 4. Analytical model. Table 2. Specifications of analytical models. Fig

Table 1. Reluctance equalization design. Fig. 2. Voltage vector of LSynRM. Fig. 4. Analytical model. Table 2. Specifications of analytical models. Fig Mover Design and Performance Analysis of Linear Synchronous Reluctance Motor with Multi-flux Barrier Masayuki Sanada, Member, Mitsutoshi Asano, Student Member, Shigeo Morimoto, Member, Yoji Takeda, Member

More information

IPSJ SIG Technical Report Vol.2015-MUS-107 No /5/23 HARK-Binaural Raspberry Pi 2 1,a) ( ) HARK 2 HARK-Binaural A/D Raspberry Pi 2 1.

IPSJ SIG Technical Report Vol.2015-MUS-107 No /5/23 HARK-Binaural Raspberry Pi 2 1,a) ( ) HARK 2 HARK-Binaural A/D Raspberry Pi 2 1. HARK-Binaural Raspberry Pi 2 1,a) 1 1 1 2 3 () HARK 2 HARK-Binaural A/D Raspberry Pi 2 1. [1,2] [2 5] () HARK (Honda Research Institute Japan audition for robots with Kyoto University) *1 GUI ( 1) Python

More information

4. C i k = 2 k-means C 1 i, C 2 i 5. C i x i p [ f(θ i ; x) = (2π) p 2 Vi 1 2 exp (x µ ] i) t V 1 i (x µ i ) 2 BIC BIC = 2 log L( ˆθ i ; x i C i ) + q

4. C i k = 2 k-means C 1 i, C 2 i 5. C i x i p [ f(θ i ; x) = (2π) p 2 Vi 1 2 exp (x µ ] i) t V 1 i (x µ i ) 2 BIC BIC = 2 log L( ˆθ i ; x i C i ) + q x-means 1 2 2 x-means, x-means k-means Bayesian Information Criterion BIC Watershed x-means Moving Object Extraction Using the Number of Clusters Determined by X-means Clustering Naoki Kubo, 1 Kousuke

More information

taro.watanabe at nict.go.jp

taro.watanabe at nict.go.jp taro.watanabe at nict.go.jp https://sites.google.com/site/alaginmt2014/ ... I want to study about machine translation. I need to master machine translation. machine translation want to study. infobox infobox

More information

IIC Proposal of Range Extension Control System by Drive and Regeneration Distribution Based on Efficiency Characteristic of Motors for Electric

IIC Proposal of Range Extension Control System by Drive and Regeneration Distribution Based on Efficiency Characteristic of Motors for Electric IIC-1-19 Proposal of Range Extension Control System by Drive and Regeneration Distribution Based on Efficiency Characteristic of Motors for Electric Vehicle Toru Suzuki, Hiroshi Fujimoto (Yokohama National

More information

(i) 1 (ii) ,, 第 5 回音声ドキュメント処理ワークショップ講演論文集 (2011 年 3 月 7 日 ) 1) 1 2) Lamel 2) Roy 3) 4) w 1 w 2 w n 2 2-g

(i) 1 (ii) ,, 第 5 回音声ドキュメント処理ワークショップ講演論文集 (2011 年 3 月 7 日 ) 1) 1  2) Lamel 2) Roy 3) 4) w 1 w 2 w n 2 2-g 1 2 1 closed Automatic Detection of Edited Parts in Inexact Transcribed Corpora Using Alignment between Edited Transcription and Corresponding Utterance Kengo Ohta, 1 Masatoshi Tsuchiya 2 and Seiichi Nakagawa

More information

IPSJ SIG Technical Report Vol.2009-BIO-17 No /5/26 DNA 1 1 DNA DNA DNA DNA Correcting read errors on DNA sequences determined by Pyrosequencing

IPSJ SIG Technical Report Vol.2009-BIO-17 No /5/26 DNA 1 1 DNA DNA DNA DNA Correcting read errors on DNA sequences determined by Pyrosequencing DNA 1 1 DNA DNA DNA DNA Correcting read errors on DNA sequences determined by Pyrosequencing Youhei Namiki 1 and Yutaka Akiyama 1 Pyrosequencing, one of the DNA sequencing technologies, allows us to determine

More information

main.dvi

main.dvi 305 8550 1 2 CREST fujii@slis.tsukuba.ac.jp 1 7% 2 2 3 PRIME Multi-lingual Information Retrieval 2 2.1 Cross-Language Information Retrieval CLIR 1990 CD-ROM a. b. c. d. b CLIR b 70% CLIR CLIR 2.2 (b) 2

More information

jpaper : 2017/4/17(17:52),,.,,,.,.,.,, Improvement in Domain Specific Word Segmentation by Symbol Grounding suzushi tomori, hirotaka kameko, takashi n

jpaper : 2017/4/17(17:52),,.,,,.,.,.,, Improvement in Domain Specific Word Segmentation by Symbol Grounding suzushi tomori, hirotaka kameko, takashi n ,,.,,,.,.,.,, Improvement in Domain Specific Word Segmentation by Symbol Grounding suzushi tomori, hirotaka kameko, takashi ninomiya, shinsuke mori and yoshimasa tsuruoka We propose a novel framework for

More information

89-95.indd

89-95.indd 解 説 機械翻訳最新事情 : ( 上 ) 統計的機械翻訳入門 永田昌明渡辺太郎塚田元 NTT 科学基礎研究所 統計的機械翻訳 (statistical machin translation) は, 互いに翻訳になっている 2 つの言語の文の対から翻訳規則や対訳辞書を自動的に学習し, 言語翻訳を実現する技術である. この技術は過去 0 年間に大きく進歩し, アラビア語と英語のような語順が比較的近い言語対では,

More information

fiš„v8.dvi

fiš„v8.dvi (2001) 49 2 333 343 Java Jasp 1 2 3 4 2001 4 13 2001 9 17 Java Jasp (JAva based Statistical Processor) Jasp Jasp. Java. 1. Jasp CPU 1 106 8569 4 6 7; fuji@ism.ac.jp 2 106 8569 4 6 7; nakanoj@ism.ac.jp

More information

DSF-517.dvi

DSF-517.dvi 1 Example Based Dialogue System Based on Satisfaction Prediction Masahiro Mizukami Nara Institute of Science and Technology masahiro-mi@is.naist.jp Lasguido Nio lasguido.kp9@is.naist.jp Hideaki Kizuki

More information

アジア言語を中心とした機械翻訳の評価 第 1 回アジア翻訳ワークショップ概要 Evaluation of Machine Translation Focusing on Asian Languages Overview of the 1st Workshop on Asian Translation

アジア言語を中心とした機械翻訳の評価 第 1 回アジア翻訳ワークショップ概要 Evaluation of Machine Translation Focusing on Asian Languages Overview of the 1st Workshop on Asian Translation アジア言語を中心とした機械翻訳の評価 第 1 回アジア翻訳ワークショップ概要 Evaluation of Machine Translation Focusing on Asian Languages Overview of the 1st Workshop on Asian Translation 国立研究開発法人科学技術振興機構 PROFILE 情報企画部研究員 国立研究開発法人情報通信研究機構

More information

(MIRU2008) HOG Histograms of Oriented Gradients (HOG)

(MIRU2008) HOG Histograms of Oriented Gradients (HOG) (MIRU2008) 2008 7 HOG - - E-mail: katsu0920@me.cs.scitec.kobe-u.ac.jp, {takigu,ariki}@kobe-u.ac.jp Histograms of Oriented Gradients (HOG) HOG Shape Contexts HOG 5.5 Histograms of Oriented Gradients D Human

More information

[1], B0TB2053, 20014 3 31. i

[1], B0TB2053, 20014 3 31. i B0TB2053 20014 3 31 [1], B0TB2053, 20014 3 31. i 1 1 2 3 2.1........................ 3 2.2........................... 3 2.3............................. 4 2.3.1..................... 4 2.3.2....................

More information

11_寄稿論文_李_再校.mcd

11_寄稿論文_李_再校.mcd 148 2011.4 1 4 Alderson 1996, Chapelle 2001, Huston 2002, Barker 2004, Rimmer 2006, Chodorow et al. 2010 He & Dai 2006 2 3 4 2 5 4 1. 2. 3. 1 2 (1) 3 90 (2) 80 1964 Brown 80 90 British National Corpus

More information

IPSJ SIG Technical Report Vol.2010-CVIM-170 No /1/ Visual Recognition of Wire Harnesses for Automated Wiring Masaki Yoneda, 1 Ta

IPSJ SIG Technical Report Vol.2010-CVIM-170 No /1/ Visual Recognition of Wire Harnesses for Automated Wiring Masaki Yoneda, 1 Ta 1 1 1 1 2 1. Visual Recognition of Wire Harnesses for Automated Wiring Masaki Yoneda, 1 Takayuki Okatani 1 and Koichiro Deguchi 1 This paper presents a method for recognizing the pose of a wire harness

More information

[2] OCR [3], [4] [5] [6] [4], [7] [8], [9] 1 [10] Fig. 1 Current arrangement and size of ruby. 2 Fig. 2 Typography combined with printing

[2] OCR [3], [4] [5] [6] [4], [7] [8], [9] 1 [10] Fig. 1 Current arrangement and size of ruby. 2 Fig. 2 Typography combined with printing 1,a) 1,b) 1,c) 2012 11 8 2012 12 18, 2013 1 27 WEB Ruby Removal Filters Using Genetic Programming for Early-modern Japanese Printed Books Taeka Awazu 1,a) Masami Takata 1,b) Kazuki Joe 1,c) Received: November

More information

IPSJ SIG Technical Report Vol.2011-EC-19 No /3/ ,.,., Peg-Scope Viewer,,.,,,,. Utilization of Watching Logs for Support of Multi-

IPSJ SIG Technical Report Vol.2011-EC-19 No /3/ ,.,., Peg-Scope Viewer,,.,,,,. Utilization of Watching Logs for Support of Multi- 1 3 5 4 1 2 1,.,., Peg-Scope Viewer,,.,,,,. Utilization of Watching Logs for Support of Multi-View Video Contents Kosuke Niwa, 1 Shogo Tokai, 3 Tetsuya Kawamoto, 5 Toshiaki Fujii, 4 Marutani Takafumi,

More information

4.1 % 7.5 %

4.1 % 7.5 % 2018 (412837) 4.1 % 7.5 % Abstract Recently, various methods for improving computial performance have been proposed. One of these various methods is Multi-core. Multi-core can execute processes in parallel

More information

新製品開発プロジェクトの評価手法

新製品開発プロジェクトの評価手法 CIRJE-J-60 2001 8 A note on new product project selection model: Empirical analysis in chemical industry Kenichi KuwashimaUniversity of Tokyo Junichi TomitaUniversity of Tokyo August, 2001 Abstract By

More information

Rapp BLEU[10] [9] BLEU OrthoBLEU Rapp OrthoBLEU [9] OrthoBLEU OrthoBLEU ) ) ) 1) NTT Natural Language Research

Rapp BLEU[10] [9] BLEU OrthoBLEU Rapp OrthoBLEU [9] OrthoBLEU OrthoBLEU ) ) ) 1) NTT Natural Language Research RJ-008 Is Back Translation Really Unuseful? Validation of Back Translation from the Perspective of a Checking Method for Users Mai Miyabe Takashi Yoshino 1. [1, 2] [3] [4] 1 2 2 [3,5,6,7] [8, 9] 1: 2 3

More information

10_08.dvi

10_08.dvi 476 67 10 2011 pp. 476 481 * 43.72.+q 1. MOS Mean Opinion Score ITU-T P.835 [1] [2] [3] Subjective and objective quality evaluation of noisereduced speech. Takeshi Yamada, Shoji Makino and Nobuhiko Kitawaki

More information

概要 単語の分散表現に基づく統計的機械翻訳の素性を提案 既存手法の FFNNLM に CNN と Gate を追加 dependency- to- string デコーダにおいて既存手法を上回る翻訳精度を達成

概要 単語の分散表現に基づく統計的機械翻訳の素性を提案 既存手法の FFNNLM に CNN と Gate を追加 dependency- to- string デコーダにおいて既存手法を上回る翻訳精度を達成 Encoding Source Language with Convolu5onal Neural Network for Machine Transla5on Fandong Meng, Zhengdong Lu, Mingxuan Wang, Hang Li, Wenbin Jiang, Qun Liu, ACL- IJCNLP 2015 すずかけ読み会奥村 高村研究室博士二年上垣外英剛 概要

More information

Vol. 42 No MUC-6 6) 90% 2) MUC-6 MET-1 7),8) 7 90% 1 MUC IREX-NE 9) 10),11) 1) MUCMET 12) IREX-NE 13) ARPA 1987 MUC 1992 TREC IREX-N

Vol. 42 No MUC-6 6) 90% 2) MUC-6 MET-1 7),8) 7 90% 1 MUC IREX-NE 9) 10),11) 1) MUCMET 12) IREX-NE 13) ARPA 1987 MUC 1992 TREC IREX-N Vol. 42 No. 6 June 2001 IREX-NE F 83.86 A Japanese Named Entity Extraction System Based on Building a Large-scale and High-quality Dictionary and Pattern-matching Rules Yoshikazu Takemoto, Toshikazu Fukushima

More information

untitled

untitled JAIS 1 2 1 2 In this paper, we focus on the pauses that partly characterize the utterances of simultaneous interpreters, and attempt to analyze the results of experiments conducted using human subjects

More information

( )

( ) NAIST-IS-MT1051071 2012 3 16 ( ) Pustejovsky 2 2,,,,,,, NAIST-IS- MT1051071, 2012 3 16. i Automatic Acquisition of Qualia Structure of Generative Lexicon in Japanese Using Learning to Rank Takahiro Tsuneyoshi

More information

自然言語処理24_705

自然言語処理24_705 nwjc2vec: word2vec nwjc2vec nwjc2vec nwjc2vec 2 nwjc2vec 7 nwjc2vec word2vec nwjc2vec: Word Embedding Data Constructed from NINJAL Web Japanese Corpus Hiroyuki Shinnou, Masayuki Asahara, Kanako Komiya

More information

IPSJ SIG Technical Report Vol.2009-DPS-141 No.20 Vol.2009-GN-73 No.20 Vol.2009-EIP-46 No /11/27 1. MIERUKEN 1 2 MIERUKEN MIERUKEN MIERUKEN: Spe

IPSJ SIG Technical Report Vol.2009-DPS-141 No.20 Vol.2009-GN-73 No.20 Vol.2009-EIP-46 No /11/27 1. MIERUKEN 1 2 MIERUKEN MIERUKEN MIERUKEN: Spe 1. MIERUKEN 1 2 MIERUKEN MIERUKEN MIERUKEN: Speech Visualization System Based on Augmented Reality Yuichiro Nagano 1 and Takashi Yoshino 2 As the spread of the Augmented Reality(AR) technology and service,

More information

Q [4] 2. [3] [5] ϵ- Q Q CO CO [4] Q Q [1] i = X ln n i + C (1) n i i n n i i i n i = n X i i C exploration exploitation [4] Q Q Q ϵ 1 ϵ 3. [3] [5] [4]

Q [4] 2. [3] [5] ϵ- Q Q CO CO [4] Q Q [1] i = X ln n i + C (1) n i i n n i i i n i = n X i i C exploration exploitation [4] Q Q Q ϵ 1 ϵ 3. [3] [5] [4] 1,a) 2,3,b) Q ϵ- 3 4 Q greedy 3 ϵ- 4 ϵ- Comparation of Methods for Choosing Actions in Werewolf Game Agents Tianhe Wang 1,a) Tomoyuki Kaneko 2,3,b) Abstract: Werewolf, also known as Mafia, is a kind of

More information

Studies of Foot Form for Footwear Design (Part 9) : Characteristics of the Foot Form of Young and Elder Women Based on their Sizes of Ball Joint Girth

Studies of Foot Form for Footwear Design (Part 9) : Characteristics of the Foot Form of Young and Elder Women Based on their Sizes of Ball Joint Girth Studies of Foot Form for Footwear Design (Part 9) : Characteristics of the Foot Form of Young and Elder Women Based on their Sizes of Ball Joint Girth and Foot Breadth Akiko Yamamoto Fukuoka Women's University,

More information

[1] [2] [3] 1 GPS 1 Twitter *1 *1 GPS [4] [5] [6] 2 [7] 1 [8] Restricted Boltzmann Machine RBM RBM

[1] [2] [3] 1 GPS 1 Twitter *1 *1 GPS [4] [5] [6] 2 [7] 1 [8] Restricted Boltzmann Machine RBM RBM 1,a) 2, 1,b) 1,c) 3,d) 1,e) 2014 2 21, 2014 9 12 2 Automatic Generation of Shogi Commentary with a Log-linear Language Model Hirotaka Kameko 1,a) Makoto Miwa 2, 1,b) Yoshimasa Tsuruoka 1,c) Shinsuke Mori

More information

IPSJ SIG Technical Report Vol.2014-DBS-159 No.6 Vol.2014-IFAT-115 No /8/1 1,a) 1 1 1,, 1. ([1]) ([2], [3]) A B 1 ([4]) 1 Graduate School of Info

IPSJ SIG Technical Report Vol.2014-DBS-159 No.6 Vol.2014-IFAT-115 No /8/1 1,a) 1 1 1,, 1. ([1]) ([2], [3]) A B 1 ([4]) 1 Graduate School of Info 1,a) 1 1 1,, 1. ([1]) ([2], [3]) A B 1 ([4]) 1 Graduate School of Information Science and Technology, Osaka University a) kawasumi.ryo@ist.osaka-u.ac.jp 1 1 Bucket R*-tree[5] [4] 2 3 4 5 6 2. 2.1 2.2 2.3

More information

No. 3 Oct The person to the left of the stool carried the traffic-cone towards the trash-can. α α β α α β α α β α Track2 Track3 Track1 Track0 1

No. 3 Oct The person to the left of the stool carried the traffic-cone towards the trash-can. α α β α α β α α β α Track2 Track3 Track1 Track0 1 ACL2013 TACL 1 ACL2013 Grounded Language Learning from Video Described with Sentences (Yu and Siskind 2013) TACL Transactions of the Association for Computational Linguistics What Makes Writing Great?

More information

,,,,., C Java,,.,,.,., ,,.,, i

,,,,., C Java,,.,,.,., ,,.,, i 24 Development of the programming s learning tool for children be derived from maze 1130353 2013 3 1 ,,,,., C Java,,.,,.,., 1 6 1 2.,,.,, i Abstract Development of the programming s learning tool for children

More information

DS0 0/9/ a b c d u t (a) (b) (c) (d) [].,., Del Barrio [], Pilato [], [].,,. [],.,.,,.,.,,.,, 0%,..,,, 0,.,.,. (variable-latency unit)., (a) ( DFG ).,

DS0 0/9/ a b c d u t (a) (b) (c) (d) [].,., Del Barrio [], Pilato [], [].,,. [],.,.,,.,.,,.,, 0%,..,,, 0,.,.,. (variable-latency unit)., (a) ( DFG )., DS0 0/9/,.,,.,,,.,.,.0%,.%.,,,, Speculative Execution in Distributed Controllers for High-Level Synthesis Shimizu iho Ishiura Nagisa bstract: This article proposes a method of incorporating speculative

More information

1 2 8 24 32 44 48 49 50 SEC journal Vol.11 No.2 Sep. 2015 1 2 SEC journal Vol.11 No.2 Sep. 2015 SEC journal Vol.11 No.2 Sep. 2015 3 4 SEC journal Vol.11 No.2 Sep. 2015 SEC journal Vol.11 No.2 Sep. 2015

More information

Vol. 9 No. 5 Oct. 2002 (?,?) 2000 6 5 6 2 3 6 4 5 2 A B C D 132

Vol. 9 No. 5 Oct. 2002 (?,?) 2000 6 5 6 2 3 6 4 5 2 A B C D 132 2000 6 5 6 :, Supporting Conference Program Production Using Natural Language Processing Technologies Hiromi itoh Ozaku Masao Utiyama Masaki Murata Kiyotaka Uchimoto and Hitoshi Isahara We applied natural

More information

Mimehand II[1] [2] 1 Suzuki [3] [3] [4] (1) (2) 1 [5] (3) 50 (4) 指文字, 3% (25 個 ) 漢字手話 + 指文字, 10% (80 個 ) 漢字手話, 43% (357 個 ) 地名 漢字手話 + 指文字, 21

Mimehand II[1] [2] 1 Suzuki [3] [3] [4] (1) (2) 1 [5] (3) 50 (4) 指文字, 3% (25 個 ) 漢字手話 + 指文字, 10% (80 個 ) 漢字手話, 43% (357 個 ) 地名 漢字手話 + 指文字, 21 1 1 1 1 1 1 1 2 transliteration Machine translation of proper names from Japanese to Japanese Sign Language Taro Miyazaki 1 Naoto Kato 1 Hiroyuki Kaneko 1 Seiki Inoue 1 Shuichi Umeda 1 Toshihiro Shimizu

More information

Vol.55 No (Jan. 2014) saccess 6 saccess 7 saccess 2. [3] p.33 * B (A) (B) (C) (D) (E) (F) *1 [3], [4] Web PDF a m

Vol.55 No (Jan. 2014) saccess 6 saccess 7 saccess 2. [3] p.33 * B (A) (B) (C) (D) (E) (F) *1 [3], [4] Web PDF   a m Vol.55 No.1 2 15 (Jan. 2014) 1,a) 2,3,b) 4,3,c) 3,d) 2013 3 18, 2013 10 9 saccess 1 1 saccess saccess Design and Implementation of an Online Tool for Database Education Hiroyuki Nagataki 1,a) Yoshiaki

More information

untitled

untitled 580 26 5 SP-G 2011 AI An Automatic Question Generation Method for a Local Councilor Search System Yasutomo KIMURA Hideyuki SHIBUKI Keiichi TAKAMARU Hokuto Ototake Tetsuro KOBAYASHI Tatsunori MORI Otaru

More information

[4], [5] [6] [7] [7], [8] [9] 70 [3] 85 40% [10] Snowdon 50 [5] Kemper [3] 2.2 [11], [12], [13] [14] [15] [16]

[4], [5] [6] [7] [7], [8] [9] 70 [3] 85 40% [10] Snowdon 50 [5] Kemper [3] 2.2 [11], [12], [13] [14] [15] [16] 1,a) 1 2 1 12 1 2Type Token 2 1 2 1. 2013 25.1% *1 2012 8 2010 II *2 *3 280 2025 323 65 9.3% *4 10 18 64 47.6 1 Center for the Promotion of Interdisciplinary Education and Research, Kyoto University 2

More information

: ( 1) () 1. ( 1) 2. ( 1) 3. ( 2)

: ( 1) () 1. ( 1) 2. ( 1) 3. ( 2) Acquiring Organized Information from News by Incremental Theme Refinements 1 1 1 Yutaro Taniguchi 1 Tetsunori Kobayashi 1 Yoshihiko Hayashi 1 1 1 School of Science and Engineering, Waseda University Abstract:

More information

A Study of Effective Application of CG Multimedia Contents for Help of Understandings of the Working Principles of the Internal Combustion Engine (The

A Study of Effective Application of CG Multimedia Contents for Help of Understandings of the Working Principles of the Internal Combustion Engine (The A Study of Effective Application of CG Multimedia Contents for Help of Understandings of the Working Principles of the Internal Combustion Engine (The Learning Effects of the Animation and the e-learning

More information

Haiku Generation Based on Motif Images Using Deep Learning Koki Yoneda 1 Soichiro Yokoyama 2 Tomohisa Yamashita 2 Hidenori Kawamura Scho

Haiku Generation Based on Motif Images Using Deep Learning Koki Yoneda 1 Soichiro Yokoyama 2 Tomohisa Yamashita 2 Hidenori Kawamura Scho Haiku Generation Based on Motif Images Using Deep Learning 1 2 2 2 Koki Yoneda 1 Soichiro Yokoyama 2 Tomohisa Yamashita 2 Hidenori Kawamura 2 1 1 School of Engineering Hokkaido University 2 2 Graduate

More information

音響モデル triphone 入力音声 音声分析 デコーダ 言語モデル N-gram bigram HMM の状態確率として利用 出力層 triphone: 3003 ノード リスコア trigram 隠れ層 2048 ノード X7 層 1 Structure of recognition syst

音響モデル triphone 入力音声 音声分析 デコーダ 言語モデル N-gram bigram HMM の状態確率として利用 出力層 triphone: 3003 ノード リスコア trigram 隠れ層 2048 ノード X7 層 1 Structure of recognition syst 1,a) 1 1 1 deep neural netowrk(dnn) (HMM) () GMM-HMM 2 3 (CSJ) 1. DNN [6]. GPGPU HMM DNN HMM () [7]. [8] [1][2][3] GMM-HMM Gaussian mixture HMM(GMM- HMM) MAP MLLR [4] [3] DNN 1 1 triphone bigram [5]. 2

More information

1 Table 1: Identification by color of voxel Voxel Mode of expression Nothing Other 1 Orange 2 Blue 3 Yellow 4 SSL Humanoid SSL-Vision 3 3 [, 21] 8 325

1 Table 1: Identification by color of voxel Voxel Mode of expression Nothing Other 1 Orange 2 Blue 3 Yellow 4 SSL Humanoid SSL-Vision 3 3 [, 21] 8 325 社団法人人工知能学会 Japanese Society for Artificial Intelligence 人工知能学会研究会資料 JSAI Technical Report SIG-Challenge-B3 (5/5) RoboCup SSL Humanoid A Proposal and its Application of Color Voxel Server for RoboCup SSL

More information

RTM RTM Risk terrain terrain RTM RTM 48

RTM RTM Risk terrain terrain RTM RTM 48 Risk Terrain Model I Risk Terrain Model RTM,,, 47 RTM RTM Risk terrain terrain RTM RTM 48 II, RTM CSV,,, RTM Caplan and Kennedy RTM Risk Terrain Modeling Diagnostics RTMDx RTMDx RTMDx III 49 - SNS 50 0

More information

Vol. 48 No. 4 Apr LAN TCP/IP LAN TCP/IP 1 PC TCP/IP 1 PC User-mode Linux 12 Development of a System to Visualize Computer Network Behavior for L

Vol. 48 No. 4 Apr LAN TCP/IP LAN TCP/IP 1 PC TCP/IP 1 PC User-mode Linux 12 Development of a System to Visualize Computer Network Behavior for L Vol. 48 No. 4 Apr. 2007 LAN TCP/IP LAN TCP/IP 1 PC TCP/IP 1 PC User-mode Linux 12 Development of a System to Visualize Computer Network Behavior for Learning to Associate LAN Construction Skills with TCP/IP

More information

2 22006 2 e-learning e e 2003 1 4 e e e-learning 2 Web e-leaning 2004 2005 2006 e 4 GP 4 e-learning e-learning e-learning e LMS LMS Internet Navigware

2 22006 2 e-learning e e 2003 1 4 e e e-learning 2 Web e-leaning 2004 2005 2006 e 4 GP 4 e-learning e-learning e-learning e LMS LMS Internet Navigware 2 2 Journal of Multimedia Aided Education Research 2006, Vol. 2, No. 2, 19 e 1 1 2 2 1 1 GP e 2004 e-learning 2004 e-learning 2005 e-learning e-learning e-learning e-learning 2004 e-learning HuWeb 2005

More information

Stepwise Chow Test * Chow Test Chow Test Stepwise Chow Test Stepwise Chow Test Stepwise Chow Test Riddell Riddell first step second step sub-step Step

Stepwise Chow Test * Chow Test Chow Test Stepwise Chow Test Stepwise Chow Test Stepwise Chow Test Riddell Riddell first step second step sub-step Step Stepwise Chow Test * Chow Test Chow Test Stepwise Chow Test Stepwise Chow Test Stepwise Chow Test Riddell Riddell first step second step sub-step Stepwise Chow Test a Stepwise Chow Test Takeuchi 1991Nomura

More information

<95DB8C9288E397C389C88A E696E6462>

<95DB8C9288E397C389C88A E696E6462> 2011 Vol.60 No.2 p.138 147 Performance of the Japanese long-term care benefit: An International comparison based on OECD health data Mie MORIKAWA[1] Takako TSUTSUI[2] [1]National Institute of Public Health,

More information

LAN LAN LAN LAN LAN LAN,, i

LAN LAN LAN LAN LAN LAN,, i 22 A secure wireless communication system using virtualization technologies 1115139 2011 3 4 LAN LAN LAN LAN LAN LAN,, i Abstract A secure wireless communication system using virtualization technologies

More information

A Study on Throw Simulation for Baseball Pitching Machine with Rollers and Its Optimization Shinobu SAKAI*5, Yuichiro KITAGAWA, Ryo KANAI and Juhachi

A Study on Throw Simulation for Baseball Pitching Machine with Rollers and Its Optimization Shinobu SAKAI*5, Yuichiro KITAGAWA, Ryo KANAI and Juhachi A Study on Throw Simulation for Baseball Pitching Machine with Rollers and Its Optimization Shinobu SAKAI*5, Yuichiro KITAGAWA, Ryo KANAI and Juhachi ODA Department of Human and Mechanical Systems Engineering,

More information