ACL RD-TEC 1.0 Summarization of N06-1037

Paper Title:
EXPLORING SYNTACTIC FEATURES FOR RELATION EXTRACTION USING A CONVOLUTION TREE KERNEL

Authors: Min Zhang and Jie Zhang and Jian Su

Other assigned terms:

  • ace corpus
  • annotation
  • annotators
  • baseline performance
  • bigram
  • case
  • chunking tree
  • classification problem
  • concept
  • concepts
  • context information
  • convolution tree
  • corpora
  • dependency graph
  • dependency tree
  • dependency trees
  • dimensionality
  • distribution
  • entity type
  • entity types
  • entropy
  • entropy models
  • experimental results
  • experimental setting
  • f-measure
  • feature
  • feature set
  • feature space
  • feature vector
  • feature vectors
  • feature weight
  • generative model
  • implementation
  • inter-annotator agreement
  • kernel function
  • knowledge
  • leaf
  • linguistic
  • linguistic features
  • maximum entropy models
  • measure
  • method
  • multi-class classification problem
  • parse
  • parse tree
  • parse tree path
  • performance comparison
  • phrase
  • phrase type
  • precision
  • process
  • production rules
  • pronoun
  • proper name
  • relation
  • root node
  • semantic
  • semantic features
  • sentence
  • similarity score
  • structural information
  • structured information
  • sub-tree
  • syntactic features
  • syntactic information
  • syntactic structure
  • syntactic structures
  • syntactic tag
  • syntactic tree
  • syntax
  • template element
  • template relation
  • terms
  • test corpus
  • test set
  • testing corpora
  • text
  • text documents
  • training
  • training set
  • tree
  • tree path
  • tree representation
  • tree structures
  • trees
  • word
  • word classes
  • word features
  • wordnet
  • words

Extracted Section Types:


This page last edited on 10 May 2017.

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