Jun-ichi Tsujii

Publications (14)11.56 Total impact

• Article: Text mining meets workflow: linking U-Compare with Taverna.

  Yoshinobu Kano, Paul Dobson, Mio Nakanishi, Jun-ichi Tsujii, Sophia Ananiadou
  Bioinformatics. 01/2010; 26:2486-2487.
• Source

  Available from: Xinglong Wang

  Conference Proceeding: Imbalanced Classification Using Dictionary-based Prototypes and Hierarchical Decision Rules for Entity Sense Disambiguation.

  Tingting Mu, Xinglong Wang, Jun-ichi Tsujii, Sophia Ananiadou
  COLING 2010, 23rd International Conference on Computational Linguistics, Posters Volume, 23-27 August 2010, Beijing, China; 01/2010
• Source

  Available from: psu.edu

  Conference Proceeding: U-Compare: An Integrated Language Resource Evaluation Platform Including a Comprehensive UIMA Resource Library.

  Yoshinobu Kano, Ruben Dorado, Luke McCrohon, Sophia Ananiadou, Jun-ichi Tsujii
  Proceedings of the International Conference on Language Resources and Evaluation, LREC 2010, 17-23 May 2010, Valletta, Malta; 01/2010
• Article: Disambiguating the species of biomedical named entities using natural language parsers.

  Xinglong Wang, Jun-ichi Tsujii, Sophia Ananiadou
  Bioinformatics. 01/2010; 26:661-667.
• Conference Proceeding: A Japanese Particle Corpus Built by Example-Based Annotation.

  Hiroki Hanaoka, Hideki Mima, Jun-ichi Tsujii
  Proceedings of the International Conference on Language Resources and Evaluation, LREC 2010, 17-23 May 2010, Valletta, Malta; 01/2010
• Source

  Available from: aclweb.org

  Conference Proceeding: Fast Full Parsing by Linear-Chain Conditional Random Fields.

  Yoshimasa Tsuruoka, Jun-ichi Tsujii, Sophia Ananiadou
  EACL 2009, 12th Conference of the European Chapter of the Association for Computational Linguistics, Proceedings of the Conference, March 30 - April3, 2009, Athens, Greece; 01/2009
• Article: FACTA: a text search engine for finding associated biomedical concepts.

  Yoshimasa Tsuruoka, Jun-ichi Tsujii, Sophia Ananiadou
  Bioinformatics. 01/2008; 24:2559-2560.
• Article: Learning string similarity measures for gene/protein name dictionary look-up using logistic regression.

  Yoshimasa Tsuruoka, John McNaught, Jun-ichi Tsujii, Sophia Ananiadou
  Bioinformatics. 01/2007; 23:2768-2774.
• Source

  Available from: dbkgroup.org

  Article: Text mining and its potential applications in systems biology.

  Sophia Ananiadou, Douglas B Kell, Jun-ichi Tsujii
  [show abstract] [hide abstract]
  ABSTRACT: With biomedical literature increasing at a rate of several thousand papers per week, it is impossible to keep abreast of all developments; therefore, automated means to manage the information overload are required. Text mining techniques, which involve the processes of information retrieval, information extraction and data mining, provide a means of solving this. By adding meaning to text, these techniques produce a more structured analysis of textual knowledge than simple word searches, and can provide powerful tools for the production and analysis of systems biology models.
  Trends in Biotechnology 01/2007; 24(12):571-9. · 9.15 Impact Factor
• Article: Thesaurus or Logical Ontology, Which One Do We Need for Text Mining?

  Jun-ichi Tsujii, Sophia Ananiadou
  Language Resources and Evaluation. 01/2005; 39:77-90.
• Article: Introduction: named entity recognition in biomedicine.

  Sophia Ananiadou, Carol Friedman, Jun-ichi Tsujii
  Journal of Biomedical Informatics. 01/2004; 37:393-395.
• Source

  Available from: manchester.ac.uk

  Article: Terminology-driven literature mining and knowledge acquisition in biomedicine.

  Goran Nenadić, Hideki Mima, Irena Spasić, Sophia Ananiadou, Jun-ichi Tsujii
  [show abstract] [hide abstract]
  ABSTRACT: In this paper we describe Tagged Information Management System (TIMS), an integrated knowledge management system for the domain of molecular biology and biomedicine, in which terminology-driven literature mining, knowledge acquisition (KA), knowledge integration (KI), and XML-based knowledge retrieval are combined using tag information management and ontology inference. The system integrates automatic terminology acquisition, term variation management, hierarchical term clustering, tag-based information extraction (IE), and ontology-based query expansion. TIMS supports introducing and combining different types of tags (linguistic and domain-specific, manual and automatic). Tag-based interval operations and a query language are introduced in order to facilitate KA and retrieval from XML documents. Through KA examples, we illustrate the way in which literature mining techniques can be utilised for knowledge discovery from documents.
  International Journal of Medical Informatics 01/2003; 67(1-3):33-48. · 2.41 Impact Factor
• Conference Proceeding: A Methodology for Terminology-based Knowledge Acquisition and Integration.

  Hideki Mima, Sophia Ananiadou, Goran Nenadic, Jun-ichi Tsujii
  01/2002
• Article: Terminology-driven literature mining and knowledge acquisition in biomedicine.

  Goran Nenadic, Hideki Mima, Irena Spasic, Sophia Ananiadou, Jun-ichi Tsujii
  I. J. Medical Informatics. 01/2002; 67:33-48.