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Yoshimasa Tsuruoka

Yoshimasa Tsuruoka

D-Index & Metrics

Computer Science

D-Index
40
Citations
7211
World Ranking
9268
National Ranking
133

Overview

Yoshimasa Tsuruoka is affiliated with the University of Tokyo in Japan. Their research spans multiple areas within computer science and engineering, with a primary focus on artificial intelligence and control systems.

The main fields of study in Tsuruoka's work include:

  • Computer Science
  • Engineering

Their subfields of study cover a range of topics:

  • Artificial Intelligence
  • Control and Systems Engineering
  • Computer Vision and Pattern Recognition
  • Molecular Biology
  • Electrical and Electronic Engineering

Tsuruoka's research interests are reflected in the main topics they have worked on:

  • Natural Language Processing Techniques
  • Topic Modeling
  • Reinforcement Learning in Robotics
  • Advanced Control Systems Optimization
  • Multimodal Machine Learning Applications
  • Fault Detection and Control Systems
  • Speech Recognition and Synthesis

The scientist has authored papers in a variety of publication venues, with several frequent outlets for their work:

  • arXiv (Cornell University)
  • Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)
  • SICE Journal of Control Measurement and System Integration
  • Journal of Natural Language Processing
  • Proceedings of the 2022 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies

Notable recent papers by Yoshimasa Tsuruoka include:

  • "mLUKE: The Power of Entity Representations in Multilingual Pretrained Language Models," 2022, Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)
  • "EASE: Entity-Aware Contrastive Learning of Sentence Embedding," 2022, Proceedings of the 2022 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies
  • "Dropout Q-Functions for Doubly Efficient Reinforcement Learning," 2021, arXiv (Cornell University)
  • "Pretraining with Artificial Language: Studying Transferable Knowledge in Language Models," 2022, Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)
  • "Computing operation procedures for chemical plants using whole-plant simulation models," 2021, Control Engineering Practice

Tsuruoka collaborates frequently with several researchers, including:

  • Takashi Onishi
  • Ryokan Ri
  • T. HIRAOKA
  • Shumpei Kubosawa
  • Ikuya Yamada

Best Publications

  • Introduction to the bio-entity recognition task at JNLPBA

    Jin-Dong Kim;Tomoko Ohta;Yoshimasa Tsuruoka;Yuka Tateisi

  • Developing a robust part-of-speech tagger for biomedical text

    Yoshimasa Tsuruoka;Yuka Tateishi;Jin-Dong Kim;Tomoko Ohta

  • A Joint Many-Task Model: Growing a Neural Network for Multiple NLP Tasks

    Kazuma Hashimoto;Caiming Xiong;Yoshimasa Tsuruoka;Richard Socher

  • Traction control of electric vehicle: basic experimental results using the test EV "UOT electric march"

    Y. Hori;Y. Toyoda;Y. Tsuruoka

  • Bidirectional Inference with the Easiest-First Strategy for Tagging Sequence Data

    Yoshimasa Tsuruoka;Jun'ichi Tsujii

  • Tree-to-Sequence Attentional Neural Machine Translation

    Akiko Eriguchi;Kazuma Hashimoto;Yoshimasa Tsuruoka

  • Stochastic Gradient Descent Training for L1-regularized Log-linear Models with Cumulative Penalty

    Yoshimasa Tsuruoka;Jun'ichi Tsujii;Sophia Ananiadou

  • FACTA: a text search engine for finding associated biomedical concepts

    Yoshimasa Tsuruoka;Jun'ichi Tsujii;Sophia Ananiadou

  • Extraction of gene-disease relations from Medline using domain dictionaries and machine learning.

    Hong-Woo Chun;Yoshimasa Tsuruoka;Jin-Dong Kim;Rie Shiba

  • Learning to Parse and Translate Improves Neural Machine Translation

    Akiko Eriguchi;Yoshimasa Tsuruoka;Kyunghyun Cho

  • Boosting Precision and Recall of Dictionary-Based Protein Name Recognition

    Yoshimasa Tsuruoka;Jun'ichi Tsujii

  • Discovering and visualizing indirect associations between biomedical concepts

    Yoshimasa Tsuruoka;Makoto Miwa;Kaisei Hamamoto;Jun'ichi Tsujii

  • Improving the performance of dictionary-based approaches in protein name recognition

    Yoshimasa Tsuruoka;Jun'ichi Tsujii

  • Game-tree Search Algorithm based on Realization Probability

    Yoshimasa Tsuruoka;Daisaku Yokoyama;Takashi Chikayama

  • Learning string similarity measures for gene/protein name dictionary look-up using logistic regression

    Yoshimasa Tsuruoka;John McNaught;Jun'i;chi Tsujii

  • Simple Customization of Recursive Neural Networks for Semantic Relation Classification

    Kazuma Hashimoto;Makoto Miwa;Yoshimasa Tsuruoka;Takashi Chikayama

  • Semantic Retrieval for the Accurate Identification of Relational Concepts in Massive Textbases

    Yusuke Miyao;Tomoko Ohta;Katsuya Masuda;Yoshimasa Tsuruoka

  • Integration of metabolic databases for the reconstruction of genome-scale metabolic networks

    Karin Radrich;Karin Radrich;Yoshimasa Tsuruoka;Yoshimasa Tsuruoka;Paul D. Dobson;Albert Gevorgyan;Albert Gevorgyan

  • Improving the Scalability of Semi-Markov Conditional Random Fields for Named Entity Recognition

    Daisuke Okanohara;Yusuke Miyao;Yoshimasa Tsuruoka;Jun'ichi Tsujii

  • Improving Chinese Word Segmentation and POS Tagging with Semi-supervised Methods Using Large Auto-Analyzed Data

    Yiou Wang;Jun'ichi Kazama;Yoshimasa Tsuruoka;Wenliang Chen

Frequent Co-Authors

Jun'ichi Tsujii
Jun'ichi Tsujii University of Manchester
Sophia Ananiadou
Sophia Ananiadou University of Manchester
Makoto Miwa
Makoto Miwa Toyota Technological Institute
Yusuke Miyao
Yusuke Miyao University of Tokyo
Tomoko Ohta
Tomoko Ohta University of Tokyo
Naoaki Okazaki
Naoaki Okazaki Tokyo Institute of Technology
Xu Sun
Xu Sun Peking University
Yoichi Hori
Yoichi Hori University of Tokyo
Hiroaki Kitano
Hiroaki Kitano Okinawa Institute of Science and Technology
Neil Swainston
Neil Swainston Epoch BioDesign

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