Earticle

초록

영어

Stemming is a natural language processing task that to remove all derivational affixes from a word. This task proved to be harder for languages with complex morphology such as the Uyghur language. This paper presents a new stemming method for Uyghur words based on CRFs (Conditional Random Fields). In the proposed method all words in the training corpus are segmented into syllables and each syllable are tagged as a part of stem or as a part of affix. We experimentally evaluated this method with five test files each includes 100 sentences , results have shown that our method gets good performance, average stemming precision, recall and F-score in open test reached 98.42%, 98.34% and 98.38% respectively.

목차

Abstract
 1. Introduction
 2. Features of Uyghur Word
  2.1. Morphologic Structure
  2.2. Syllabic Structure
  2.3. Phonetic Changes
 3. Data Preparation
  3.1. Syllable Segmentation
  3.2. Syllable Tagging
 4. CRFs Stemming
  4.1. Problem Definition
  4.2. CRFs Training
  4.3. Testing and Post-processing
 5. Evaluating
 6. Conclusion
 Acknowledgements
 References

키워드

저자

• Abdurahim Mahmoud [ Institute of Information Science and Engineering, Xinjiang University, Urumqi, China ]
• Akbar Pattar [ Institute of Information Science and Engineering, Xinjiang University, Urumqi, China ]
• Askar Hamdulla [ School of Software, Xinjiang University, Urumqi, China ] Corresponding author

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