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

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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? First Experiments on Article Quality Prediction in the Science Journalism Domain (Louis and Nenkova 2013) ACL2013 ACL2013 TACL 2012 ACL ACL2013 TACL TACL 2 Grounded Language Learning from Video Described with Sentences (Yu and Siskind 2013) 3-5 person(α) carried(α, β) α β, Tohoku University ACL NAACL EACL EMNLP 4 2 9

No. 3 Oct. 2013 The person to the left of the stool carried the traffic-cone towards the trash-can.

2 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 HMM Yamato (Yamato, Ohya, and Ishii 1992) HMM HMM HMM jump 2 HMM quickly 1 HMM jump quickly 3 2

ACL2013 F (D t,j t ) X $ G(D t 1,j t 1,D t,j t ) towards

3 ACL2013 F (D t,j t ) X $ G(D t 1,j t 1,D t,j t ) towards carry 2 ACL$ person(p 0 ) to-the-left-of(p 0, p 1 ) stool(p 1 ) carried(p 0, p 2 ) detector detector 2 4 detector F G 2 carry towards HMM HMM 2 HMM Ghahramani (Ghahramani, I. Jordan, and Smyth 1997) Factorial HMM HMM EM 4 daiti-m/paper/snlp2013-video. pdf 3

4 No. 3 Oct NLP What Makes Writing Great? First Experiments on Article Quality Prediction in the Science Journalism Domain (Louis and Nenkova 2013) (Brill and Moore 2000; Tetreault and Chodorow 2008; Rozovskaya and Roth 2010) New York Times New York Times The Best American Writing SVM bag of words coherence EMNLP A coherence model based on syntactic patterns (Louis and Nenkova 2012) daiti-m/paper/snlp2013-video.pdf 4

5 ACL ACL TACL 5 NLP Brill, E. and Moore, R. C. (2000). An improved error model for noisy channel spelling correction. In Proceedings of the 38th Annual Meeting of the Association for Computational Linguistics, pp Associationg for Conmputational Linguistics. Ghahramani, Z., I. Jordan, M., and Smyth, P. (1997). Factorial Hidden Markov Models. In Machine Learning. MIT Press. Louis, A. and Nenkova, A. (2012). A coherence model based on syntactic patterns. In Proceedings of the 2012 Joint Conference on Empirical Methods in Natural Language Processing and Computational Natural Language Learning, EMNLP-CoNLL 12, pp Stroudsburg, PA, USA. Association for Computational Linguistics. Louis, A. and Nenkova, A. (2013). What Makes Writing Great? First Experiments on Article Quality Prediction in the Science Journalism Domain. In Transactions of Association for Computational Linguistics. Association for Computational Linguistics. Rozovskaya, A. and Roth, D. (2010). Generating confusion sets for context-sensitive error correction. In Proceedings of the 2010 Conference on Empirical Methods in Natural Language Processing, EMNLP 10, pp Stroudsburg, PA, USA. Association for Computational 5

6 No. 3 Oct Linguistics. Tetreault, J. R. and Chodorow, M. (2008). The ups and downs of preposition error detection in ESL writing. In Proceedings of the 22nd International Conference on Computational Linguistics - Volume 1, COLING 08, pp Stroudsburg, PA, USA. Association for Computational Linguistics. Yamato, J., Ohya, J., and Ishii, K. (1992). Recognizing Human Action in Time-Sequential Images using Hidden Markov Model. In IEEE Computer Society Conference on Computer Vision and Pattern Recognition. Yu, H. and Siskind, J. M. (2013). Grounded Language Learning from Video Described with Sentences. In Proceedings of the 51st Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pp Sofia, Bulgaria. Association for Computational Linguistics

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