Extracting Protein-Protein Interaction Information from Biomedical Text with SVM(Natural Language Processing)

スポンサーリンク

概要

• 論文の詳細を見る

• Automated information extraction systems from biomedical text have been reported. Some systems are based on manually developed rules or pattern matching. Manually developed rules are specific for analysis, however, new rules must be developed for each new domain. Although the corpus must be developed by human effort, a machine-learning approach automatically learns the rules from the corpus. In this article, we present a system for automatically extracting protein-protein interaction information from biomedical text with support vector machines (SVMs). We describe the performance of our system and compare its ability to extract protein-protein interaction information with that of other systems.

• 社団法人電子情報通信学会の論文
• 2006-08-01

著者

• Murata Masaki

  National Inst. Information And Communications Technol. Kyoto Jpn

• MITSUMORI Tomohiro

  Graduate School of the Information Science, Nara Institute of Science and Technology

• FUKUDA Yasushi

  Sony-Kihara Research Center Inc.

• DOI Kouichi

  Graduate School of the Information Science, Nara Institute of Science and Technology

• DOI Hirohumi

  Graduate School of the Information Science, Nara Institute of Science and Technology

• Murata Masaki

  National Institute Of Information And Communications Technology

• Doi Kouichi

  Graduate School Of The Information Science Nara Institute Of Science And Technology

• Murata Masaki

  National Inst. Information And Communications Technol.

• Doi Hirohumi

  Graduate School Of The Information Science Nara Institute Of Science And Technology

• Mitsumori Tomohiro

  Graduate School Of The Information Science Nara Institute Of Science And Technology

関連論文

• Extracting Protein-Protein Interaction Information from Biomedical Text with SVM(Natural Language Processing)
• Use of Multiple Documents as Evidence with Decreased Adding in a Japanese Question-answering System
• Automatic F-term Classification of Japanese Patent Documents Using the k-Nearest Neighborhood Method and the SMART Weighting
• Toolbar to Highlight Important Expressions in Scientific Articles on Atomic and Molecular Physics
• Acquisition of Data for Plasma Simulation by Automated Extraction of Terminology from Article Abstracts
• Automatic F-term Classification of Japanese Patent Documents Using the k-Nearest Neighborhood Method and the SMART Weighting
• Use of Multiple Documents as Evidence with Decreased Adding in a Japanese Question-answering System

スポンサーリンク