ACL RD-TEC 1.0 Summarization of P06-1061

Paper Title:
SEGMENT-BASED HIDDEN MARKOV MODELS FOR INFORMATION EXTRACTION

Authors: Zhenmei Gu and Nick Cercone

Other assigned terms:

  • alphabet
  • approach
  • association for computational linguistics
  • case
  • concept
  • context models
  • context size
  • data set
  • data sparseness
  • data sparseness problem
  • distribution
  • document
  • entropy
  • estimation
  • events
  • exact match
  • experimental results
  • extraction evaluation
  • extraction process
  • fact
  • frequency distribution
  • ie evaluation
  • ie problem
  • ie task
  • interpolation
  • labeling
  • likelihood
  • linguistics
  • markov models
  • measure
  • measures
  • method
  • model parameter
  • model parameters
  • natural language
  • noise
  • performance comparison
  • performance evaluation
  • precision
  • probabilities
  • probability
  • probability distribution
  • procedure
  • process
  • punctuation
  • punctuation marks
  • retrieval performance
  • retrieval precision
  • segment boundary
  • segments
  • sentence
  • sentence boundaries
  • sentences
  • slot
  • sparseness problem
  • state labeling
  • statistical models
  • style
  • symbol
  • symbols
  • tags
  • technique
  • term
  • term distribution
  • terms
  • text
  • text segment
  • text segments
  • tokens
  • topology
  • training
  • training data
  • training document
  • training documents
  • training examples
  • transition probabilities
  • transition probability
  • vocabulary
  • word
  • words

Extracted Section Types:


This page last edited on 10 May 2017.

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