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2010 International Conference on Intelligent Computing and Integrated Systems

作者： Xin Yu Institute of Software Chinese Academy of Sciences Graduate University of the Chinese Academy of Sciences Beijing China Jian Wu Institute of Software Chinese Academy of Sciences Beijing China Weina Zhao Beijing Language and Culture University Beijing China

＜正＞In creation of the Chinese-Tibetan parallel corpus, research of alignment at sentence level is an essential topic. At present,there is no research on Chinese-Tibetan sentence *** paper proposes an automatic approach for Chinese-Tibetan sentence alignment which is based on *** is a solution to the specific problems encountered in Chinese-Tibetan sentence alignment,which are sentence recognition and problems caused by different granularity between Chinese word segmentation and Tibetan word segmentation in sentence alignment process. Experiment shows the precision of this approach is 84.8%.

关键词： Chinese-Tibetan sentence alignment dictionary-based parallel corpora Tibetan information processing Tibetan sentence recognition

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The Enhancement of Arabic Stemming by Using Light Stemming and dictionary-based Stemming

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Journal of Software Engineering and Applications 2011年第9期4卷 522-526页

作者： Yasir Alhanini Mohd Juzaiddin Ab Aziz 不详

Word stemming is one of the most important factors that affect the performance of many natural language processing applications such as part of speech tagging, syntactic parsing, machine translation system and information retrieval systems. Computational stemming is an urgent problem for Arabic Natural Language Processing, because Arabic is a highly inflected language. The existing stemmers have ignored the handling of multi-word expressions and identification of Arabic names. We used the enhanced stemming for extracting the stem of Arabic words that is based on light stemming and dictionary-based stemming approach. The enhanced stemmer includes the handling of multiword expressions and the named entity recognition. We have used Arabic corpus that consists of ten documents in order to evaluate the enhanced stemmer. We reported the accuracy values for the enhanced stemmer, light stemmer, and dictionary-based stemmer in each document. The results obtain shows that the average of accuracy in enhanced stemmer on the corpus is 96.29%. The experimental results showed that the enhanced stemmer is better than the light stemmer and dictionary-based stemmer that achieved highest accuracy values.

关键词： dictionary-based Stemmer Arabic Morphological Analyzer Named Entity Recognition

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