(Under construction)

Contact information

Address Nara Institute of Science and Technology
Division of Information Science
8916-5 Takayama, Ikoma, Nara 630-0192, Japan
Office location Information Science Bldg. A, Room A703
Phone +81 743 72 5241
Fax +81 743 72 5249
E-mail shimbo at is.naist.jp

Courses

2018 Quarter I - 3010 Artificial intelligence

Recent publications

V.-T. Phi, J. Santoso, M. Shimbo, and Y. Matsumoto. Ranking-based automatic seed selection and noise reduction for weakly supervised relation extraction. In Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (ACL '18). Melbourne, Australia. 2018. To appear.

Y. Kobayashi, M. Shimbo, and Y. Matsumoto. Citation recommendation using distributed representation of discourse facets in scientific articles. In Proceedings of the 2018 ACM/IEEE Joint Conference on Digital Libraries (JCDL '18). Fortworth, Texas, USA. 2018. pp. 243–251. Paper

T. Ishihara, K. Hayashi, H. Manabe, M. Shimbo, and Masaaki Nagata. Neural tensor networks with diagonal slice matrices. In Proceedings of the 16th Annual Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies (NAACL-HLT '18). New Orleans, Louisiana, USA. 2018. pp. 506–515. Paper

H. Manabe, K. Hayashi, and M. Shimbo. Data-dependent learning of symmetric/anti-symmetric relations for knowledge base completion. In Proceedings of the 32nd AAAI Conference on Artificial Intelligence (AAAI '18). pp. 3754–3761. New Orleans, Louisiana, USA. 2018. Paper

T. Hamaguchi, H. Oiwa, M. Shimbo, and Y. Matsumoto. Knowledge transfer for out-of-knowledge-base entities: a graph neural network approach. In Proceedings of the 26th International Joint Conference on Artificial Intelligence (IJCAI '17): Main Track, pp. 1802–1808. Melbourne, Australia. 2017. Paper

K. Hayashi and M. Shimbo. On the equivalence of holographic and complex embeddings. In Proceedings of the 55th Annual Meeting of Association for Computational Linguistics (ACL '17): Short Papers, pp. 554–559. Vancouver, British Columbia, Canada. 2017. Paper | Poster.

Y. Shigeto, M. Shimbo, and Y. Matsumoto. A fast and easy regression technique for k-NN classification without using negative pairs. In Proceedings of the 21st Pacific-Asia Conference on Knowledge Discovery and Data Mining (PAKDD '17), Part I, pp. 17–29. Jeju, Korea. 2017. Springer LNCS 10234. Publisher page.

F. Fouss, M. Saerens, and M. Shimbo. Algorithms and Models for Network Data and Link Analysis. Cambridge University Press. 2016. CUP | Amazon.com | Amazon.co.jp

M. Tsubaki, K. Duh, M. Shimbo, and Y. Matsumoto. Non-linear similarity learning for compositionality. In Proceedings of the 30th AAAI Conference on Artificial Intelligence (AAAI '16), pp. 2828–2834. Phoenix, Arizona, USA. 2016. Paper.

Y. Shigeto, I. Suzuki, K. Hara, M. Shimbo, and Y. Matsumoto. Ridge regression, hubness, and zero-shot learning. In Proceedings of the 2015 European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases (ECMLPKDD '15), Part I, pp. 135–151. Porto, Portugal. 2015. Springer LNCS 9284. Best student paper runner-up. Publisher page.

A. Yoshimoto, K. Hara, M. Shimbo, and Y. Matsumoto. Coordination-aware dependency parsing (preliminary report). In Proceedings of the 14th International Conference on Parsing Technologies (IWPT '15), pp. 66–70. Bilbao, the Basque Country. 2015.

K. Hara, I. Suzuki, M. Shimbo, K. Kobayashi, K. Fukumizu, and M. Radovanović. Localized centering: reducing hubness in large-sample data. In Proceedings of the 29th AAAI Conference on Artificial Intelligence (AAAI '15), pp. 2645–2651. Austin, Texas, USA. 2015.

M. Tsubaki, K. Duh, M. Shimbo, and Y. Matsumoto. Modeling and learning semantic co-compositionality through prototype projections and neural networks. In Proceedings of the 2013 Conference on Empirical Methods in Natural Language Processing (EMNLP '13), pp. 130–140. Seattle, Washington, USA. 2013.

I. Suzuki, K. Hara, M. Shimbo, M. Saerens, and K. Fukumizu. Centering similarity measures to reduce hubs. In Proceedings of the 2013 Conference on Empirical Methods in Natural Language Processing (EMNLP '13), pp. 613–623. Seattle, Washington, USA. 2013.

K. Hara, I. Suzuki, M. Shimbo, and Y. Matsumoto. Walk-based computation of contextual word similarity. In Proceedings of the 24th International Conference on Computational Linguistics (COLING '12), pp. 1081–1096, Mumbai, India. 2012

I. Suzuki, K. Hara, M. Shimbo, Y. Matsumoto, and M. Saerens. Investigating the effectiveness of Laplacian-based kernels in hub reduction. In Proceedings of the 26th AAAI Conference on Artificial Intelligence (AAAI '12), pp. 1112–1118. Toronto, Ontario, Canada. 2012.

K. Ozaki, M. Shimbo, M. Komachi, and Y. Matsumoto. Using the mutual k-nearest neighbor graphs for semi-supervised classification of natural language data. In Proceedings of the 15th Conference on Computational Natural Language Learning (CoNLL '11), pp. 154–162. Portland, Oregon, USA. 2011.

A. Mantrach, N. van Zeebroeck, P. Francq, M. Shimbo, H. Bersini, and M. Saerens. Semi-supervised classification and betweenness computation on large, sparse, directed graphs. Pattern Recognition, Vol. 44, No. 6, pp. 1212–1224. 2011.

S. García-Díez, F. Fouss, M. Shimbo, and M. Saerens. A sum-over-paths extension of edit distances accounting for all sequence alignments. Pattern Recognition, Vol. 44, No. 6, pp. 1172–1182. 2011.