Content Tree Word Embedding for document representation

@article{Kamkarhaghighi2017ContentTW,
  title={Content Tree Word Embedding for document representation},
  author={Mehran Kamkarhaghighi and Masoud Makrehchi},
  journal={Expert Syst. Appl.},
  year={2017},
  volume={90},
  pages={241-249}
}
Only humans can understand and comprehend the actual meaning that underlies natural written language, whereas machines can form semantic relationships only after humans have provided the parameters that are necessary to model the meaning. To enable computer models to access the underlying meaning in written language, accurate and sufficient document representation is crucial. Recently, word embedding approaches have drawn much attention in text mining research. One of the main benefits of such… CONTINUE READING

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