Study on Semantic Markup of Species Description Text in Chinese Based on Auto-learning Rules
Duan Yufeng1, Hei Zhenzhen1, Ju Fei1, Cui Hong2
1. Business School, East China Normal University, Shanghai 200241, China;
2. School of Information Resource & Library Science, University of Arizona, Tucson 85719, USA
Abstract：This paper uses the algorithm of auto-learning rules combining with leading words to implement the semantic markup of species description text in Chinese with the data set of 1 000 documents collected from Flora of China randomly. Experimental results indicate that the whole markup efficiency (the values of F) of rule-based algorithm, which is designed by the study, generally reaches 0.930, and most elements are in the range of 0.724-0.964. Therefore, this algorithm is better than Naive Bayesian categorization algorithm, and it is also proved that leading words are positive for optimizing the algorithm.
段宇锋, 黑珍珍, 鞠菲, 崔红. 基于自主学习规则的中文物种描述文本的语义标注研究[J]. 现代图书情报技术, 2012, 28(5): 41-47.
Duan Yufeng, Hei Zhenzhen, Ju Fei, Cui Hong. Study on Semantic Markup of Species Description Text in Chinese Based on Auto-learning Rules. New Technology of Library and Information Service, DOI：10.11925/infotech.1003-3513.2012.05.06.
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