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RIKEN Center for Advanced Intelligence Project

Knowledge Acquisition Team

Team Leader: Yuji Matsumoto (D.Eng.)
Yuji  Matsumoto(D.Eng.)

A huge amount of knowledge is being produced along with the advance of scientific fields. Such expert knowledge is partly organized in domain databases, but a large portion of it is still kept in scholarly documents. It is time-consuming and cumbersome to construct domain databases and to conduct content-aware retrieval of scholarly documents. The Knowledge Acquisition Team aims at text/figure analysis and knowledge extraction from scholarly documents and also at development of infrastructure for content-aware retrieval of scholarly documents through domain database completion, knowledge organization, relation and summarization technologies.

Main Research Field

Computer Science

Related Research Fields

Multidisciplinary

Research Subjects

  • Natural Language Processing
  • Knowledge Acquisition
  • Machine Learning

Selected Publications

  1. Ouchi, H., Shindo, H., Matsumoto, Y.:
    "Neural Modeling of Multi-Predicate Interactions for Japanese Predicate Argument Structure Analysis"
    Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics (ACL), Volume 1, pp.1591-1600 (2017).
  2. *Kato, A., Shindo, H., Matsumoto, Y.:
    "English Multiword Expression-aware Dependency Parsing Including Named Entities"
    Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics (ACL), Volume 2, pp.427-432 (2017).
  3. *Hamaguchi, T., Oiwa, H., Shimbo, M., Matsumoto, Y.:
    "Knowledge Transfer for Out-of-Knowledge-Base Entities: A Graph Neural Network Approach"
    Proceedings of the Twenty-Sixth International Joint Conference on Artificial Intelligence (IJCAI), Main track. Pp.1802-1808 (2017).
  4. Sato, M., Manabe, H., Noji, H., Matsumoto, Y.:
    "Adversarial Training for Cross-Domain Universal Dependency Parsing"
    Proceedings of the CoNLL 2017 Shared Task: Multilingual Parsing from Raw Text to Universal Dependencies, pp.71-79 (2017)
  5. *Tsubaki, M., Shimbo, M., Matsumoto, Y.:
    "Protein Fold Recognition with Representation Learning and Long Short-Term Memory"
    IPSJ Transactions on Bioinformatics, Vol.10. pp. 2-8, (2017).
  6. *Ouchi, H., Duh, K., Shindo, H., Matsumoto, Y.:
    "Transition-Based Dependency Parsing Exploiting Supertags"
    IEEE/ACM Transactions on Audio, Speech, and Language Processing, Vol.24, Issue 11, pp.2059-2068 (2016).
  7. *Pereira, L., Manguilimotan, E., Matsumoto, Y.:
    "Leveraging a Large Learner Corpus for Automatic Suggestion of Collocations for Learners of Japanese as a Second Language"
    CALICO (The Computer Assisted Language Instruction Consortium) Journal, Vol.33, No.3, pp.311-333 (2016).
  8. *Tsubaki, M., Duh, K., M., Shimbo, M., Matsumoto, Y.:
    "Non-Linear Similarity Learning for Compositionality"
    Proceeding of the Thirtieth AAAI Conference on Artificial Intelligence (AAAI), pp.2828-2834 (2016).
  9. *Yoshikawa, M., Shindo, H., Matsumoto, Y.:
    "Joint Transition-based Dependency Parsing and Disfluency Detection for Automatic Speech Recognition Texts"
    Proceedings of Conference on Empirical Methods in Natural Language Processing (EMNLP), pp.1036-1041 (2016).
  10. *Liu, X., Duh, K., Matsumoto, Y.:
    "Multilingual Topic Models for Bilingual Dictionary Extraction"
    ACM Transactions on Asian and Low-Resource Language Information Processing (TALLIP), Volume 14 Issue 3, Article No.11, pp.1-22 (2015).

Contact information

Information Science Building A,
8916-5 Takayama, Ikoma, Nara, 630-0192 Japan
Tel: +81-(0)743-72-5240
Fax: +81-(0)743-72-5249

Email: matsu [at] is.naist.jp

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