Termset weighting by adapting term weighting schemes to utilize cardinality statistics for binary text categorization

@article{Badawi2017TermsetWB,
  title={Termset weighting by adapting term weighting schemes to utilize cardinality statistics for binary text categorization},
  author={Dima Badawi and Hakan Altinçay},
  journal={Applied Intelligence},
  year={2017},
  volume={47},
  pages={456-472}
}
This study proposes a novel scheme for termset weighting based on cardinality statistics. Specifically, termsets are evaluated by considering the number of apparent member terms. Based on a recently verified hypothesis that the occurrence of a subset of terms may also transfer worthwhile information about class memberships, the existing term weighting schemes are adapted. Here, the weight of a given termset is computed as the product of two factors. The first is a function of the member term… CONTINUE READING

Similar Papers

Citations

Publications citing this paper.
SHOWING 1-2 OF 2 CITATIONS

An Evaluation of Character Level N-gram Termsets in Text Categorization

  • 2018 International Conference on Artificial Intelligence and Data Processing (IDAP)
  • 2018
VIEW 6 EXCERPTS
CITES METHODS & BACKGROUND
HIGHLY INFLUENCED

References

Publications referenced by this paper.
SHOWING 1-10 OF 48 REFERENCES

Supervised and Traditional Term Weighting Methods for Automatic Text Categorization

  • IEEE Transactions on Pattern Analysis and Machine Intelligence
  • 2009
VIEW 3 EXCERPTS
HIGHLY INFLUENTIAL

Martı́nez-Carranza J (2015) Term-weighting learning via genetic programming for text classification

HJ Escalante, MA Garcı́a-Limȯn, +3 authors EF Morales
  • 2015