• DocumentCode
    2259574
  • Title

    A bi-threshold model for PP-attachment disambiguation through backing off to 2-tuples directly

  • Author

    Liao, Bosen ; Luo, Huiqiong ; Xiao, Luying

  • Author_Institution
    Sch. of Comput. Sci. & Eng., Univ. of Electron. Sci. & Technol. of China, Chengdu, China
  • fYear
    2009
  • fDate
    24-27 Sept. 2009
  • Firstpage
    1
  • Lastpage
    5
  • Abstract
    We present a bi-threshold approach to prepositional phrase attachment disambiguation which is based on a pseudo-backed-off model through backing off to 2-tuples directly. We use 2-tuples counts only to make attachment decision relied on two threshold comparisons. The model alleviates the sparse data problem because 4-tuples and 3-tuples counts are not used. It also achieves good performance with 85.02% accuracy on the test data of the IBM data set. Our experiments show that 4-tuples and 3-tuples counts are unnecessary for resolving prepositional phrase attachment.
  • Keywords
    data handling; learning (artificial intelligence); natural language processing; 2-tuples backoff; IBM test data; PP-attachment disambiguation; bi-threshold model; prepositional phrase attachment; pseudo-backed-off model; sparse data problem; Computer science; Data mining; Decision trees; Dictionaries; Entropy; Natural language processing; Nearest neighbor searches; Testing; Training data; Wheels; PP-attachment disambiguation; backed-off model; bi-threshold approach; pseudo-backed-off model;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Natural Language Processing and Knowledge Engineering, 2009. NLP-KE 2009. International Conference on
  • Conference_Location
    Dalian
  • Print_ISBN
    978-1-4244-4538-7
  • Electronic_ISBN
    978-1-4244-4540-0
  • Type

    conf

  • DOI
    10.1109/NLPKE.2009.5313759
  • Filename
    5313759