ACL RD-TEC 1.0 Summarization of W06-3309

Paper Title:
GENERATIVE CONTENT MODELS FOR STRUCTURAL ANALYSIS OF MEDICAL ABSTRACTS

Authors: Jimmy Lin and Damianos Karakos and Dina Demner-Fushman and Sanjeev Khudanpur

Other assigned terms:

  • annotation
  • annotation process
  • approach
  • backoff
  • benchmark
  • bias
  • bigram
  • case
  • classification accuracy
  • classification error
  • classification error rate
  • classification performance
  • classification task
  • classification tasks
  • corpora
  • decision-making process
  • device
  • discourse
  • discourse structure
  • document
  • error rate
  • estimation
  • evaluations
  • experimental results
  • f-measure
  • fact
  • generative models
  • genre
  • hypothesis
  • language modeling toolkit
  • language models
  • language processing applications
  • lexical features
  • likelihood
  • linear complexity
  • linguistics
  • mapping
  • markov models
  • measure
  • measures
  • medline
  • metadata
  • modeling toolkit
  • natural language
  • natural language processing applications
  • ontologies
  • precision
  • probabilities
  • probability
  • procedure
  • process
  • projection
  • query
  • questionnaire
  • semantic
  • semantic features
  • sentence
  • sentences
  • standard benchmark
  • statistics
  • support vector
  • svms
  • symbol
  • technique
  • term
  • terms
  • test collection
  • text
  • text genre
  • toolkit
  • trained model
  • training
  • training data
  • training examples
  • training set
  • transition probability
  • word
  • word sequences
  • words

Extracted Section Types:


This page last edited on 10 May 2017.

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