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Developing an Algorithm for Mining Semantics in Texts

  • Minhua Huang
  • Robert M. Haralick
Part of the Lecture Notes in Computer Science book series (LNCS, volume 7182)

Abstract

This paper discusses an algorithm for identifying semantic arguments of a verb, word senses of a polysemous word, noun phrases in a sentence. The heart of the algorithm is a probabilistic graphical model. In contrast with other existed graphical models, such as Naive Bayes models, CRFs, HMMs, and MEMMs, this model determines a sequence of optimal class assignments among M choices for a sequence of N input symbols without using dynamic programming, running fast–O(MN), and taking less memory space–O(M). Experiments conducted on standard data sets show encourage results.

Keywords

semantics algorithm text pattern probabilistic graphical model semantic argument word sense NP chunk 

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Copyright information

© Springer-Verlag Berlin Heidelberg 2012

Authors and Affiliations

  • Minhua Huang
    • 1
  • Robert M. Haralick
    • 1
  1. 1.Computer Science DepartmentThe Graduate School and University Center, The City University of New YorkNew YorkUSA

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