| Implementing “A generative theory of tonal music” M Hamanaka, K Hirata, S Tojo Journal of New Music Research 35 (4), 249-277, 2006 | 213 | 2006 |
| Melody morphing method based on GTTM M Hamanaka, K Hirata, S Tojo ICMC, 155-158, 2008 | 76 | 2008 |
| Recurrent neural network-based models for recognizing requisite and effectuation parts in legal texts TS Nguyen, LM Nguyen, S Tojo, K Satoh, A Shimazu Artificial Intelligence and Law 26 (2), 169-199, 2018 | 73 | 2018 |
| Encoded summarization: summarizing documents into continuous vector space for legal case retrieval V Tran, M Le Nguyen, S Tojo, K Satoh Artificial Intelligence and Law 28 (4), 441-467, 2020 | 66 | 2020 |
| Musical structural analysis database based on GTTM M Hamanaka, K Hirata, S Tojo ISMIR 2014, 2014 | 58 | 2014 |
| FATTA: Full automatic time-span tree analyzer M Hamanaka, K Hirata, S Tojo ICMC 1, 153-156, 2007 | 57 | 2007 |
| ATTA: Automatic Time-Span Tree Analyzer Based on Extended GTTM. M Hamanaka, K Hirata, S Tojo ISMIR 5, 358-365, 2005 | 54 | 2005 |
| Implementing Methods for Analysing Music Based on Lerdahl and Jackendoff’s Generative Theory of Tonal Music M Hamanaka, K Hirata, S Tojo Computational music analysis, 221-249, 2015 | 44 | 2015 |
| Neural-based natural language generation in dialogue using rnn encoder-decoder with semantic aggregation VK Tran, M Le Nguyen, S Tojo Proceedings of the 18th annual SIGdial meeting on discourse and dialogue …, 2017 | 38 | 2017 |
| GTTM III: Learning-Based Time-Span Tree Generator Based on PCFG M Hamanaka, K Hirata, S Tojo International Symposium on Computer Music Multidisciplinary Research, 387-404, 2015 | 38 | 2015 |
| Analysis of Chord Progression by HPSG. S Tojo, Y Oka, M Nishida Artificial Intelligence and Applications, 305-310, 2006 | 34 | 2006 |
| New HELIC-II: A software tool for legal reasoning K Nitta, M Shibasaki, T Sakata, T Yamaji, W Xianchang, H Ohsaki, S Tojo, ... Proceedings of the 5th international conference on artificial intelligence …, 1995 | 34 | 1995 |
| Automatic Generation of Grouping Structure based on the GTTM M Hamanaka, K Hirata, S Tojo ICMC, 2004 | 33 | 2004 |
| deepgttm-iii: Multi-task learning with grouping and metrical structures M Hamanaka, K Hirata, S Tojo International Symposium on Computer Music Multidisciplinary Research, 238-251, 2017 | 29 | 2017 |
| Wide-coverage relation extraction from MEDLINE using deep syntax NTH Nguyen, M Miwa, Y Tsuruoka, T Chikayama, S Tojo BMC bioinformatics 16 (1), 107, 2015 | 28 | 2015 |
| Interactive Gttm Analyzer. M Hamanaka, S Tojo ISMIR, 291-296, 2009 | 27 | 2009 |
| An order-sorted resolution with implicitly negative sorts K Kaneiwa, S Tojo International Conference on Logic Programming, 300-314, 2001 | 26 | 2001 |
| Cognitive Similarity grounded by tree distance from the analysis of K. 265/300e K Hirata, S Tojo, M Hamanaka International Symposium on Computer Music Multidisciplinary Research, 589-605, 2013 | 24 | 2013 |
| Melody extrapolation in GTTM approach M Hamanaka, K Hirata, S Tojo ICMC, 2009 | 24 | 2009 |
| deepGTTM-I&II: Local boundary and metrical structure analyzer based on deep learning technique M Hamanaka, K Hirata, S Tojo International Symposium on Computer Music Multidisciplinary Research, 3-21, 2016 | 23 | 2016 |
