Journal of Information Processing Systems

Korea Information Processing Society (한국정보처리학회)
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Semantic Feature Analysis for Multi-Label Text Classification on Topics of the Al-Quran Verses

  • Received : 2021.07.05
  • Accepted : 2022.09.29
  • Published : 2024.02.29
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Abstract

Nowadays, Islamic content is widely used in research, including Hadith and the Al-Quran. Both are mostly used in the field of natural language processing, especially in text classification research. One of the difficulties in learning the Al-Quran is ambiguity, while the Al-Quran is used as the main source of Islamic law and the life guidance of a Muslim in the world. This research was proposed to relieve people in learning the Al-Quran. We proposed a word embedding feature-based on Tensor Space Model as feature extraction, which is used to reduce the ambiguity. Based on the experiment results and the analysis, we prove that the proposed method yields the best performance with the Hamming loss 0.10317.

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References

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