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article · Procedia Computer Science · 2024

Paraphrasing identification Using ACV-tree kernel

Marwah Alian

Cited by 1Open access

Abstract

The aim of paraphrasing identification techniques is to identify if two sentences or texts have the same meaning; even if they do not contain a number of identical phrases or words. In this paper we proposed a hybrid technique based on using attention constituency vector (ACV)-tree kernel in short texts or sentence similarity computation. Then a similarity threshold is used to identifying if these sentences are paraphrased or not. The experiments are conducted on Arabic paraphrasing benchmark. The proposed method provides a recall of 70% and a precision of 76%, when the determined threshold is 0.5, while a recall of 94% and a precision of 0.751 achieved using a threshold of 0.3.

Cite this paper

APA 7

Alian, M. (2024). Paraphrasing identification Using ACV-tree kernel. Procedia Computer Science, 244, 151–157. https://doi.org/10.1016/j.procs.2024.10.188

MLA 9

Alian, Marwah. “Paraphrasing identification Using ACV-tree kernel.” Procedia Computer Science, vol. 244, 2024, pp. 151–57, https://doi.org/10.1016/j.procs.2024.10.188.

Chicago

Alian, Marwah. 2024. “Paraphrasing identification Using ACV-tree kernel.” Procedia Computer Science 244: 151–57. https://doi.org/10.1016/j.procs.2024.10.188.

Harvard

Alian, M. (2024) ‘Paraphrasing identification Using ACV-tree kernel’, Procedia Computer Science, 244, pp. 151–157. Available at: https://doi.org/10.1016/j.procs.2024.10.188.

BibTeX
@article{alian2024paraphrasing,
  title = {{Paraphrasing identification Using ACV-tree kernel}},
  author = {Alian, Marwah},
  journal = {Procedia Computer Science},
  volume = {244},
  pages = {151--157},
  year = {2024},
  doi = {10.1016/j.procs.2024.10.188}
}

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