| A* CCG Parsing with a Supertag and Dependency Factored Model [in Japanese] M Yoshikawa, H Noji, Y Matsumoto 自然言語処理 26 (1), 83-119, 2019 | 87 | 2019 |
| Joint transition-based dependency parsing and disfluency detection for automatic speech recognition texts M Yoshikawa Nara Institute of Science and Technology, 2017 | 48 | 2017 |
| Multimodal logical inference system for visual-textual entailment R Suzuki, H Yanaka, M Yoshikawa, K Mineshima, D Bekki arXiv preprint arXiv:1906.03952, 2019 | 19 | 2019 |
| Do Deep Neural Networks Capture Compositionality in Arithmetic Reasoning? K Kudo, Y Aoki, T Kuribayashi, A Brassard, M Yoshikawa, K Sakaguchi, ... arXiv preprint arXiv:2302.07866, 2023 | 12 | 2023 |
| Combining axiom injection and knowledge base completion for efficient natural language inference M Yoshikawa, K Mineshima, H Noji, D Bekki Proceedings of the AAAI Conference on Artificial Intelligence 33 (01), 7410-7417, 2019 | 12 | 2019 |
| Instance-based neural dependency parsing H Ouchi, J Suzuki, S Kobayashi, S Yokoi, T Kuribayashi, M Yoshikawa, ... Transactions of the Association for Computational Linguistics 9, 1493-1507, 2021 | 5 | 2021 |
| Automatic generation of high quality CCGbanks for parser domain adaptation M Yoshikawa, H Noji, K Mineshima, D Bekki arXiv preprint arXiv:1906.01834, 2019 | 5 | 2019 |
| Neural sentence generation from formal semantics K Manome, M Yoshikawa, H Yanaka, P Martínez-Gómez, K Mineshima, ... Proceedings of the 11th International Conference on Natural Language …, 2018 | 5 | 2018 |
| Tracing and Manipulating Intermediate Values in Neural Math Problem Solvers Y Matsumoto, B Heinzerling, M Yoshikawa, K Inui arXiv preprint arXiv:2301.06758, 2023 | 3 | 2023 |
| Empirical investigation of neural symbolic reasoning strategies Y Aoki, K Kudo, T Kuribayashi, A Brassard, M Yoshikawa, K Sakaguchi, ... arXiv preprint arXiv:2302.08148, 2023 | 2 | 2023 |
| Consistent CCG parsing over multiple sentences for improved logical reasoning M Yoshikawa, K Mineshima, H Noji, D Bekki arXiv preprint arXiv:1804.07068, 2018 | 2 | 2018 |
| Virtual Human Generative Model: Masked Modeling Approach for Learning Human Characteristics K Oono, N Charoenphakdee, K Bito, Z Gao, H Igata, M Yoshikawa, Y Ota, ... arXiv preprint arXiv:2306.10656, 2023 | 1 | 2023 |
| Creating a General-Purpose Generative Model for Healthcare Data based on Multiple Clinical Studies H Maruyama, K Bito, Y Saito, M Hibi, S Katada, A Kawakami, K Oono, ... medRxiv, 2025.01. 23.25320504, 2025 | | 2025 |
| Control system, control method, and control program T Hirai, K Kanuma, H Hino, Y Yoshimura, K Uehara, A Kinoshita, M Sakai, ... US Patent App. 17/973,868, 2023 | | 2023 |
| Studies on Efficient Parsing and Logic-based Inference based on Combinatory Categorial Grammar M Yoshikawa Nara Institute of Science and Technology, 2020 | | 2020 |
| Structural Change Point Detection Using A Large Random Matrix and Sparse Modeling. K Ito, A Kinoshita, M Yoshikawa EDBT/ICDT Workshops, 2019 | | 2019 |
| Neural sentence generation from formal semantics D Bekki, P Martínez-Gómez, M Yoshikawa, K Mineshima, K Manome, ... Proceedings of the 11th International Conference on Natural Language Generation, 2018 | | 2018 |
| Similarity Matrix Model for the NTCIR-12 MedNLPDoc Task. Y Sawai, M Omura, H Ouchi, Y Nagai, M Yoshikawa, I Yamada NTCIR, 2016 | | 2016 |
| テキスト情報と画像情報を組み合わせた論理推論システムの構築 R Suzuki, M Yoshikawa, H Yanaka, K Mineshima, D Bekki | | |
Koji MineshimaKeio UniversityVerified email at abelard.flet.keio.ac.jp