jpaper : 2017/4/17(17:52),,.,,,.,.,.,, Improvement in Domain Specific Word Segmentation by Symbol Grounding suzushi tomori, hirotaka kameko, takashi n
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1 ,,.,,,.,.,.,, 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 improving a word segmenter using information acquired from symbol grounding. The framework uses a dataset consisting of pairs of non-textual information and a commentary. We generate a pseudo-stochastically segmented corpus from the commentaries, and then build a neural network to predict relationships between non-textual information and the words. We generate a domain specific term dictionary by using the neural network for word segmenter. We applied our method to game records of Japanese chess with commentaries. The experimental results show that the accuracy of a word segmenter can be improved by incorporating the generated dictionary. Key Words: symbol grounding, word segmentation, dictionary, Graduate School of Informatics, Kyoto University, Graduate School of Engineering, The University of Tokyo, Graduate School of Science and Engineering, Ehime University, Academic Center for Computing and Media Studies, Kyoto University
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4 Vol. 13 No. 2 April ,,, ( 2009).,.,,,., 1,,.,,..,. For i = 1 to n r 1 (1) x i (2) 0 < p < 1 p (3) if p < P i : otherwise: m, x i x i+1 m P i. m, P i P i 0. 3,., (Mori et al. 2016)., (Regneri, Rohrbach, Wetzel, Thater, Schiele, and Pinkal 2013) ,.,. 4
5 ,,,,,,.,,. 3.2 S i (i = 1,..., n) C i, S i f(s i ). n., C i C i. C i m C ij (j = 1,..., m),, ( m = 4 ). f(s i ) C i ,. d (d ),,. 1, 0, 2., Bag-of-Words 3. (Tsuruoka, Yokoyama, and Chikayama 2002),.. a: b: c: a b d: c, 2 ( 2 ) 3, d,. a, b, c, 94.7%, 87.9%.,,..,,,. 5
6 Vol. 13 No. 2 April , (Neubig, Nakata, and Mori 2011). x = x 1,..., x nr. (Fan et al. 2008) P i = 1 P i = 0. 6 n-gram n-gram (n = 1, 2, 3)., n-gram. 4.2,...,,.,., S i d.,. sum d, R%. max d, R%. each d R%., S 1, S 2 [,,,, ] 5, 40%. S 1 [1.4, 1.5, 0.2, 0.5, 3.8] S 2 [4.9, 0.8, 0.1, 0.9, 3.2] 6
7 ,,,, 1 ( ) 33, ,151 BCCWJ-train 56, ,951 1,911, , , , , , ,941 0 ( ) 253 3,898 4, BCCWJ-test 6, , ,261 0 ( ) 3,000 21,261 26,767 0 ( ) 1,788 31,220 41, sum S 1, S 2 [6.3, 2.3, 0.3, 1.4, 7.0] 40%. max [4.9, 1.5, 0.2, 0.9, 3.8]. each [1.4, 1.5, 0.2, 0.5, 3.8] [4.9, 0.8, 0.1, 0.9, 3.2] 40%. ( ).,. 5 4., ( ) , ( ) / /., 33,151.,., ( 7
8 Vol. 13 No. 2 April 2006 )., (BCCWJ) (Maekawa, Yamazaki, Ogiso, Maruyama, Ogura, Kashino, Koiso, Yamaguchi, Tanaka, and Den 2014), ( ),. BCCWJ,. 5, 041, (253 ), (3, 000 ), (1, 788 ) 3. 1,.,.,, : UniDic (234, 652 ) 2. + :,. + :,. UniDic. UniDic,., 1. P i, P i 0.5, P i. m = 4, 4.,, R %,., (sum, max, each) R, R (F ). 1 /
9 ,,,, 2 BCCWJ (6, 025 ) F 99.36% 96.37% % 99.35% (4, 788 ) F 90.78% 91.03% % 91.53% % 91.57% R = 0.074, 110., each R = 0.074, ,, F. = = F = BCCWJ, 3.. BCCWJ ( 2) ( 3),.. 3,, 1%. (Liu, Zhang, Che, Liu, and Wu 2014).. 3,, 9
10 Vol. 13 No. 2 April (3, 000 ) F 90.90% 88.91% % 89.51% % 89.44% (1, 788 ) F 90.70% 92.47% % 92.91% % 93.03% ,.,,.,.., , 2,,..,.,, ( 5).,.,.,, 2., (4, 788 ) (F ). 12,
11 ,,,,!"#&)%!"#&(%!"#&'%!!"!"#&&%!"#&"%!"#&%!"#"!%!"#"$% *% '***% +***%!***% "&***% ")***% "$***% &"***% &(***% &,***% '"***% ''")"%!"#$ 2 6. (Nagata 1994)., Sproat (Sproat, Gale, Shih, and Chang 1996).., Neubig (Neubig et al. 2011),, BI. BIES (Xue 2003). BIES,,, 1. BI BIES CRF. 1., BIES CRF.., (Yang and Vozila 2014)(Jiang, Sun, Lü, Yang, and Liu 2013)(Liu et al. 2014).,, CRF., Tsuboi 11
12 Vol. 13 No. 2 April 2006 (Tsuboi, Kashima, Mori, Oda, and Matsumoto 2008), Mori (Mori and Nagao 1996). (Roy and Pentland 2002; Nguyen, Vogel, and Smith 2010). Roy,.,,. Nguyen..,,. 7,.,,..,., (Ma and Hinrichs 2015),. JSPS K00293, Fan, R.-E., Chang, K.-W., Hsieh, C.-J., Wang, X.-R., and Lin, C.-J. (2008). LIBLINEAR: A Library for Large Linear Classification. Journal of Machine Learning Research, 9, pp Farhadi, A., Hejrati, M., Sadeghi, M. A., Young, P., Rashtchian, C., Hockenmaier, J., and Forsyth, D. (2010). Every Picture Tells a Story: Generating Sentences from Images. In 12
13 ,,,, Proceedings of the 11th European Conference on Computer Vision, pp Jiang, W., Sun, M., Lü, Y., Yang, Y., and Liu, Q. (2013). Discriminative Learning with Natural Annotations: Word Segmentation as a Case Study. In Proceedings of the 51st Annual Meeting of the Association for Computational Linguistics, pp Kiros, R., Salakhutdinov, R., and Zemel, R. (2014). Multimodal Neural Language Models. In Proceedings of the 31st International Conference on Machine Learning, pp Liu, Y., Zhang, Y., Che, W., Liu, T., and Wu, F. (2014). Domain Adaptation for CRF-based Chinese Word Segmentation using Free Annotations. In Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing, pp Ma, J. and Hinrichs, E. W. (2015). Accurate Linear-Time Chinese Word Segmentation via Embedding Matching. In Proceedings of the 53rd Annual Meeting of the Association for Computational Linguistics, pp Maekawa, K., Yamazaki, M., Ogiso, T., Maruyama, T., Ogura, H., Kashino, W., Koiso, H., Yamaguchi, M., Tanaka, M., and Den, Y. (2014). Balanced corpus of contemporary written Japanese. Language Resources and Evaluation, 48, pp Mori, S. and Nagao, M. (1996). Word Extraction from Corpora and Its Part-of-Speech Estimation Using Distributional Analysis. In Proceedings of the 16th International Conference on Computational Linguistics, pp Mori, S., Richardson, J., Ushiku, A., Sasada, T., Kameko, H., and Tsuruoka, Y. (2016). A Japanese Chess Commentary Corpus. In Proceedings of the Tenth International Conference on Language Resources and Evaluation, pp Mori, S. and Takuma, D. (2004). Word n-gram probability estimation from a Japanese raw corpus. In Proceedings of the Eighth International Conference on Speech and Language Processing, pp Nagata, M. (1994). A Stochastic Japanese Morphological Analyzer Using a forward-dp backward-a* N-best Search Algorithm. In Proceedings of the 15th Conference on Computational Linguistics, pp Neubig, G., Nakata, Y., and Mori, S. (2011). Pointwise Prediction for Robust, Adaptable Japanese Morphological Analysis. In Proceedings of the 49th Annual Meeting of the Association for Computational Linguistics: Human Language Technologies, pp Nguyen, T., Vogel, S., and Smith, N. A. (2010). Nonparametric Word Segmentation for Machine Translation. In Proceedings of the 23rd International Conference on Computational Linguistics, pp Regneri, M., Rohrbach, M., Wetzel, D., Thater, S., Schiele, B., and Pinkal, M. (2013). Ground- 13
14 Vol. 13 No. 2 April 2006 ing Action Descriptions in Videos. Transactions of the Association for Computational Linguistics, 1 (Mar), pp Rohrbach, M., Qiu, W., Titov, I., Thater, S., Pinkal, M., and Schiele, B. (2013). Translating Video Content to Natural Language Descriptions. In Proceedings of the 14th International Conference on Computer Vision, pp Roy, D. K. and Pentland, A. P. (2002). Learning words from sights and sounds: a computational model. Cognitive Science, 26 (1), pp Sproat, R., Gale, W., Shih, C., and Chang, N. (1996). A Stochastic Finite-state Wordsegmentation Algorithm for Chinese. Computational Linguistics, 22 (3), pp Tsuboi, Y., Kashima, H., Mori, S., Oda, H., and Matsumoto, Y. (2008). Training Conditional Random Fields Using Incomplete Annotations. In Proceedings of the 22nd International Conference on Computational Linguistics, pp Tsuruoka, Y., Yokoyama, D., and Chikayama, T. (2002). Game-Tree Search Algorithm Based On Realization Probability. Journal of the International Computer Games Association, 25, p Xue, N. (2003). Chinese Word Segmentation as Character Tagging. The Association for Computational Linguistics and Chinese Language Processing, 8, pp Yang, F. and Vozila, P. (2014). Semi-Supervised Chinese Word Segmentation Using Partial- Label Learning With Conditional Random Fields. In Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing, pp Yang, Y., Teo, C. L., Daumé, III, H., and Aloimonos, Y. (2011). Corpus-guided Sentence Generation of Natural Images. In Proceedings of 2011 the Conference on Empirical Methods in Natural Language Processing, pp (2009).., 16 (5), pp ,
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Key Words: probabilisic scenario earthquake, active fault data, Great Hanshin earthquake, low frequency-high impact earthquake motion, seismic hazard map 3) Cornell, C. A.: Engineering Seismic
Attendance Demand for J-League õ Shinsuke KAWAI* and Takeo HIRATA* Abstract The purpose of this study was to clarify the variables determining the attendance in J-league matches, using the 2,699 J-league
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
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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,