Earticle

초록

영어

Many important applications have used novelty detection in order to reduce redundant and non-relevant information presented to users of the document retrieval systems. In this study, sentence level information patterns are proposed for enhancing the novelty detection for Arabic text documents. Two models based on sentence level information patterns are suggested and compared; the first one is based on sentence length while the second one is based on opinion patterns. Experimental results have showed that both of the proposed models; Length Adjusted (LA) model and Length and Opinion Adjusted (LOA) model, can significantly improve the performance of novelty detection for Arabic texts, in terms of precision at top ranks. Better results were provided by LA model over LOA model. This shows that the sentence length is more important for enhancing the novelty detection than other suggested sentence level information patterns (e.g. opinion patterns).

목차

Abstract
 1. Introduction
 2. Related Works
 3. Methodology
  3.1 Relevant Sentence Retrieval
  3.2 Novel Sentence Extraction
 4. Results and Evaluation
 5. Conclusion and Future Work
 References

키워드

저자

• Mohammed Al-Kabi [ Zarqa University ]
• Esra’a AL-Shdaifat [ Hashemite University ]
• Emad Al-Shawakfa [ Yarmouk University ]
• Abdullah Wahbeh [ Dakota State University ]
• Izzat Alsmadi [ Yarmouk University ]

참고문헌

발행기관

간행물

표지보기 관심저널 등록

• 간행물명

  International Journal of Signal Processing, Image Processing and Pattern Recognition

• 간기

  격월간

• pISSN

  2005-4254

• 수록기간

  2008~2016

• 십진분류

  KDC 505 DDC 605