A Probabilistic Graphical Model for Recognizing NP Chunks in Texts

  • Minhua Huang
  • Robert M. Haralick
Part of the Lecture Notes in Computer Science book series (LNCS, volume 5459)


We present a probabilistic graphical model for identifying noun phrase patterns in texts. This model is derived from mathematical processes under two reasonable conditional independence assumptions with different perspectives compared with other graphical models, such as CRFs or MEMMs. Empirical results shown our model is effective. Experiments on WSJ data from the Penn Treebank, our method achieves an average of precision 97.7% and an average of recall 98.7%. Further experiments on the CoNLL-2000 shared task data set show our method achieves the best performance compared to competing methods that other researchers have published on this data set. Our average precision is 95.15% and an average recall is 96.05%.


NP chunking graphical models cliques separators 


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Copyright information

© Springer-Verlag Berlin Heidelberg 2009

Authors and Affiliations

  • Minhua Huang
    • 1
  • Robert M. Haralick
    • 1
  1. 1.Computer Science, Graduate CenterCity University of New YorkNew YorkUSA

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