Enhancing the Takhrij Al-Hadith based on Contextual Similarity using BERT Embeddings

Muslims are required to conduct Takhrij to validate the truth of Hadith text, especially when it is obtained from online media. Typically, the traditional Takhrij processes are conducted by experts and apply to Arabic Hadith text. This study introduces a contextual similarity model based on BERT Embedding to handle Takhrij on Indonesian Hadith Text. This study examines the effectiveness of BERT Fine-Tuning on the six pre-trained models to produce embedding models. The result shows that BERT Fine-Tuning improves the embedding model average accuracy by 47.67%, with a mean of 0.956845. The most high-grade accuracy was the BERT embedding built based on the indobenchmark/indobert-large-p2 pre-trained model on 1.00. In addition, the manual evaluation achieved 91.67% accuracy.

@article{Luthfi2021,
  title     = {Enhancing the Takhrij Al-Hadith based on Contextual Similarity using BERT Embeddings},
  journal   = {International Journal of Advanced Computer Science and Applications},
  volume    = {12},
  number    = {11},
  year      = {2021},
  publisher = {The Science and Information Organization},
  author    = {Emha Taufiq Luthfi and Zeratul Izzah Mohd Yusoh and Burhanuddin Mohd Aboobaider},
  doi       = {10.14569/IJACSA.2021.0121133},
  url       = {https://doi.org/10.14569/IJACSA.2021.0121133}
}