원문정보
보안공학연구지원센터(IJSIP)
International Journal of Signal Processing, Image Processing and Pattern Recognition
Vol.8 No.8
2015.08
pp.43-50
피인용수 : 0건 (자료제공 : 네이버학술정보)
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
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
저자정보
참고문헌
자료제공 : 네이버학술정보