| Dependency tree-based sentiment classification using CRFs with hidden variables T Nakagawa, K Inui, S Kurohashi Human Language Technologies: The 2010 Annual Conference of the North …, 2010 | 486 | 2010 |
| Collecting evaluative expressions for opinion extraction N Kobayashi, K Inui, Y Matsumoto, K Tateishi, T Fukushima Natural Language Processing–IJCNLP 2004: First International Joint …, 2005 | 397 | 2005 |
| Extracting aspect-evaluation and aspect-of relations in opinion mining N Kobayashi, K Inui, Y Matsumoto Proceedings of the 2007 Joint Conference on Empirical Methods in Natural …, 2007 | 260 | 2007 |
| Attention is not only a weight: Analyzing transformers with vector norms G Kobayashi, T Kuribayashi, S Yokoi, K Inui arXiv preprint arXiv:2004.10102, 2020 | 241 | 2020 |
| Emotion classification using massive examples extracted from the web R Tokuhisa, K Inui, Y Matsumoto Proceedings of the 22nd International Conference on Computational …, 2008 | 238 | 2008 |
| 意見抽出のための評価表現の収集 小林のぞみ， 乾健太郎， 松本裕治， 立石健二， 福島俊一 自然言語処理 12 (3), 203-222, 2005 | 237 | 2005 |
| Text simplification for reading assistance: a project note K Inui, A Fujita, T Takahashi, R Iida, T Iwakura Proceedings of the second international workshop on Paraphrasing, 9-16, 2003 | 220 | 2003 |
| Selective sampling for example-based word sense disambiguation A Fujii, K Inui, T Tokunaga, H Tanaka arXiv preprint cs/9910020, 1999 | 206 | 1999 |
| An empirical study of incorporating pseudo data into grammatical error correction S Kiyono, J Suzuki, M Mita, T Mizumoto, K Inui arXiv preprint arXiv:1909.00502, 2019 | 180 | 2019 |
| Encoder-decoder models can benefit from pre-trained masked language models in grammatical error correction M Kaneko, M Mita, S Kiyono, J Suzuki, K Inui arXiv preprint arXiv:2005.00987, 2020 | 171 | 2020 |
| Annotating a Japanese text corpus with predicate-argument and coreference relations R Iida, M Komachi, K Inui, Y Matsumoto Proceedings of the linguistic annotation workshop, 132-139, 2007 | 152 | 2007 |
| Neural architectures for fine-grained entity type classification S Shimaoka, P Stenetorp, K Inui, S Riedel arXiv preprint arXiv:1606.01341, 2016 | 151 | 2016 |
| 述語の選択選好性に着目した名詞評価極性の獲得 東山昌彦， ヒガシヤママサヒコ 奈良先端科学技術大学院大学, 2008 | 142 | 2008 |
| What makes reading comprehension questions easier? S Sugawara, K Inui, S Sekine, A Aizawa arXiv preprint arXiv:1808.09384, 2018 | 140 | 2018 |
| Language models as knowledge bases: On entity representations, storage capacity, and paraphrased queries B Heinzerling, K Inui arXiv preprint arXiv:2008.09036, 2020 | 139 | 2020 |
| Experience mining: Building a large-scale database of personal experiences and opinions from web documents K Inui, S Abe, K Hara, H Morita, C Sao, M Eguchi, A Sumida, K Murakami, ... 2008 IEEE/WIC/ACM International Conference on Web Intelligence and …, 2008 | 117 | 2008 |
| Incorporating contextual cues in trainable models for coreference resolution R Iida, K Inui, H Takamura, Y Matsumoto Proceedings of the EACL Workshop on the Computational Treatment of Anaphora …, 2003 | 106 | 2003 |
| REALTIME QA: what's the answer right now? J Kasai, K Sakaguchi, R Le Bras, A Asai, X Yu, D Radev, NA Smith, ... Advances in Neural Information Processing Systems 36, 2024 | 100 | 2024 |
| An attentive neural architecture for fine-grained entity type classification S Shimaoka, P Stenetorp, K Inui, S Riedel arXiv preprint arXiv:1604.05525, 2016 | 99 | 2016 |
| Exploiting syntactic patterns as clues in zero-anaphora resolution R Iida, K Inui, Y Matsumoto Proceedings of the 21st International Conference on Computational …, 2006 | 97 | 2006 |
Yuji MatsumotoTeam Leader, RIKEN Center for Advanced Intelligence ProjectVerified email at is.naist.jp