• DocumentCode
    442046
  • Title

    Machine learning for automatic acquisition of Chinese linguistic ontology knowledge

  • Author

    Zheng, De-quan ; Zhao, Tie-jun ; Yu, Feng ; Sheng-Li

  • Author_Institution
    Sch. of Comput. Sci. & Eng., Harbin Univ. of Commerce, China
  • Volume
    6
  • fYear
    2005
  • fDate
    18-21 Aug. 2005
  • Firstpage
    3728
  • Abstract
    Due to the complexity and flexibility of natural language, automatic linguistic knowledge acquisition and its application research becomes difficult. In this paper, we present a machine learning method to automatically acquire Chinese linguistic ontology knowledge from typical corpus. This study, first, defined the description frame of Chinese linguistic ontology knowledge, and then, automatically acquired the usage of a Chinese word with its co-occurrence of context in using semantic, pragmatics, syntactic, etc from the corpus, final, the above information and their representation act as Chinese linguistic ontology knowledge bank. We completed two groups of experiments, i.e. documents similarity computing, text reordering for information retrieval. Compared with previous works, the proposed method solves the inferior precision of nature language processing.
  • Keywords
    knowledge acquisition; learning (artificial intelligence); linguistics; natural languages; ontologies (artificial intelligence); Chinese linguistic ontology knowledge; document similarity computing; information retrieval; knowledge acquisition; machine learning; natural language processing; text reordering; Computer science; Information retrieval; Knowledge acquisition; Knowledge engineering; Learning systems; Machine learning; Natural language processing; Natural languages; Ontologies; Tagging; Machine learning; knowledge acquisition; linguistic ontology knowledge; natural language processing;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Machine Learning and Cybernetics, 2005. Proceedings of 2005 International Conference on
  • Conference_Location
    Guangzhou, China
  • Print_ISBN
    0-7803-9091-1
  • Type

    conf

  • DOI
    10.1109/ICMLC.2005.1527589
  • Filename
    1527589