(String-to-Tree ) KJ [11] best 1-best 2. SMT 2. [9] Brockett [2] Mizumoto [10] Brockett [2] [10] [15] ê = argmax e P(e f ) = argmax e M m=1 λ

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1 1,a) 1,b) 2 2 Shared Task 4 n-best 1-best 1. Web SNS Lang-8 *1 GIN- GER *2 2 HOO [7], [8] CoNLL Shared Task 2013 [14] 2014 CoNLL Shared Task 1 Rozovskaya and Roth [16] Tajiri [19] Rozovskaya and Roth [17] 2 1 Konan-JIEM [12] *3 KJ 1 Nara Institute of Science and Technology a) tomoya-m@is.naist.jp b) matsu@is.naist.jp *1 *2 *3 1 Konan-JIEM (%) (%) NUS Corpus of Learner English [6] CoNLL Shared Task [6] NUCLE wrong collocation/idiom/preposition 7,312 local redundancies 6,390 article or determiner 6,004 noun number 3, [1], [2], [3], [10], [20] Brockett [2] Mizumoto [10] Behera and Bhattacharyya [1] Buys and Merwe [3] [11] 1

2 (String-to-Tree ) KJ [11] best 1-best 2. SMT 2. [9] Brockett [2] Mizumoto [10] Brockett [2] [10] [15] ê = argmax e P(e f ) = argmax e M m=1 λ m h m (e, f ) (1) e f h m (e, f ) M λ m f e P( f e) P(e) n-gram [11] 3.1 F true positive, false positive, false negative true positive false positive false negative *4 1 1 to to = 1/2 = 1/2 = 1/3 = 1/2 3.2 cicada 0.30 *5 expgram 0.20 *6 5-gram ZMERT *7 F 2 True negative rate (TNR) T NR = true negative (true negative + f alse positive) Lang-8 Learner Corpora v2.0 *8 SNS Lang-8 Lang-8 2 SNS Lang-8 Lang-8 *4 *5 trans/ cicada/ *6 trans/ expgram/ *7 ozaidan/zmert/ *8 (2) 2

3 He talked to me his life of Kyoto, and he took me Kyoto university. He talked to me about his life in Kyoto and he took me to Kyoto university. He talked me his life on Kyoto, and he took me to Kyoto university. 1 Learner Corpora 5 630,117 Konan-JIEM EDCW2012 * ,411 EDCW best F 10-best 1-best 3 10-best 1 1, [5] [4] [18] Maximum-Entropy Perceptron *9 2 KJ 10-best F (.798) (.925) (.857) (.717) (.950) (.817) (.556) (.811) (.660) (.530) (.739) (.618) (.261) (.677) (.377) (.308) (.608) (.409) (.436) (.783) (.560) (.730) (.875) (.796) (.550) (.822) (.659) (.692) (.844) (.761) (.462) (.720) (.563) (.352) (.864) (.500) (.250) (.160) (.195) (.536) (.652) (.588) (.368) (.636) (.467) (.000) (.000) (.000) (.182) (.667) (.286) (.000) (.000) (.000) (.680) (.582) (.627) 3

4 4 Moreover, music is necessary for me. 1 Moreover, the music is necessary for me. 2 ( ) Moreover, music is necessary for me. 5 I wondered they make me study reading and whiting then. I wondered they made me study reading and whiting then. 1 I wondered if they make me study reading and whiting then. 6 ( ) I wondered if they made me study reading and whiting then. I don t read a book for a long time. I haven t read a book for a long time. 1 I don t read a book for a long time. 2 ( ) I haven t read a book for a long time , music the 1 a the 3 10-best best made make wondered then 2 for a long time 6 are a funny book be play watch eat to play watch watching eat eating n-best 1-best 4

5 6 If there are a funny book, I may read one. If there are funny books, I may read one. 1 If there are a funny book, I may read one. 9 ( ) If there are funny books, I may read one. 7 For example, that is to play sports, watch TV and eat dinner with my friends, and so on. For example, they are to play sports, watch TV and eat dinner with my friends, and so on. For example, that is to play sports, watching TV, eating dinner with my friends, and so on. 1 [13] Lang-8 [1] Behera, B. and Bhattacharyya, P.: Automated Grammar Correction Using Hierarchical Phrase-Based Statistical Machine Translation, Proceedings of IJCNLP, pp (2013). [2] Brockett, C., Dolan, W. B. and Gamon, M.: Correcting ESL Errors Using Phrasal SMT Techniques, Proceedings of COLING-ACL, pp (2006). [3] Buys, J. and van der Merwe, B.: A Tree Transducer Model for Grammatical Error Correction, Proceedings of CoNLL Shared Task, pp (2013). [4] Charniak, E. and Johnson, M.: Coarse-to-fine N-best Parsing and MaxEnt Discriminative Reranking, Proceedings of ACL, pp (2005). [5] Collins, M.: Discriminative Reranking for Natural Language Parsing, Proceedings of ICML, pp (2000). [6] Dahlmeier, D., Ng, H. T. and Wu, S. M.: Building a Large Annotated Corpus of Learner English: The NUS Corpus of Learner English, Proceedings of the Eighth Workshop on Innovative Use of NLP for Building Educational Applications, pp (2013). [7] Dale, R., Anisimoff, I. and Narroway, G.: HOO 2012: A Report on the Preposition and Determiner Error Correction Shared Task, Proceedings of BEA, pp (2012). [8] Dale, R. and Kilgarriff, A.: Helping Our Own: The HOO 2011 Pilot Shared Task, Proceedings of ENLG, pp (2011). [9] Koehn, P., Och, F. J. and Marcu, D.: Statistical Phrase-Based Translation, Proceedings of HLT-NAACL, pp (2003). [10] Mizumoto, T., Hayashibe, Y., Komachi, M., Nagata, M. and Matsumoto, Y.: The Effect of Learner Corpus Size in Grammatical Error Correction of ESL Writings, Proceedings of COLING, pp (2012). [11] 20 pp (2014). [12] Nagata, R., Whittaker, E. and Sheinman, V.: Creating a manually error-tagged and shallow-parsed learner corpus, Proceedings of ACL-HLT, pp (2011). [13]. D, Vol. 96, No. 5, pp [14] Ng, H. T., Wu, S. M., Wu, Y., Hadiwinoto, C. and Tetreault, J.: The CoNLL-2013 Shared Task on Grammatical Error Correction, Proceedings of CoNLL Shared Task, pp (2013). [15] Och, F. J. and Ney, H.: Discriminative Training and Maximum Entropy Models for Statistical Machine Translation, Proceedings of ACL, pp (2002). [16] Rozovskaya, A. and Roth, D.: Algorithm Selection and Model Adaptation for ESL Correction Tasks, Proceedings of ACL, pp (2011). [17] Rozovskaya, A. and Roth, D.: Joint Learning and Inference for Grammatical Error Correction, Proceedings of EMNLP, pp (2013). [18] Shen, L., Sarkar, A. and Och, F. J.: Discriminative Reranking for Machine Translation, Proceedings of HLT-NAACL, pp (2004). [19] Tajiri, T., Komachi, M. and Matsumoto, Y.: Tense and Aspect Error Correction for ESL Learners Using Global Context, Proceedings of ACL, pp (2012). [20] Yuan, Z. and Felice, M.: Constrained Grammatical Error Correction using Statistical Machine Translation, Proceedings of CoNLL Shared Task, pp (2013). 5

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