Research

Interactively Picking Real-World Objects with Unconstrained Spoken Language Instructions (ICRA-2018)

Jun Hatori, Yuta Kikuchi, Sosuke Kobayashi, Kuniyuki Takahashi, Yuta Tsuboi, Yuya Unno, Wilson Ko, Jethro Tan. Proceedings of International Conference on Robotics and Automation (ICRA), 2018.

Project page:
https://projects.preferred.jp/interactive-robot/

Github:
https://pfnet.github.io/interactive-robot/

Incremental Joint Approach to Word Segmentation, POS Tagging and Dependency Parsing in Chinese (ACL-2012)

Jun Hatori, Takuya Matsuzaki, Yusuke Miyao, Jun’ichi Tsujii. Incremental Joint Approach to Chinese Word Segmentation, POS Tagging, and Dependency Parsing. In the Proceedings of the 50th Annual Meeting for the Association of Computational Linguistics (ACL-2012). Jeju, Korea. 2012.

Abstract:
We propose the first joint model for word segmentation, POS tagging, and dependency parsing for Chinese. Based on an extension of the incremental joint model for POS tagging and dependency parsing (Hatori et al., 2011), we propose an efficient character-based decoding method that can combine features from state-of-the-art segmentation, POS tagging, and dependency parsing models. We also describe our method to align comparable states in the beam, and how we can combine features of different characteristics in our incremental framework. In experiments using the Chinese Treebank (CTB), we show that the accuracies of the three tasks can be improved significantly over the baseline models, particularly by 0.6% for POS tagging and 2.4% for dependency parsing. We also perform comparison experiments with the partially joint models.

Paper:
http://www.aclweb.org/anthology/P12-1110

Source:
https://github.com/junhtr/corbit