Semantic Annotation of Species Description Text in Chinese by Combining Naïve Bayes Algorithm with Bootstrapping Method
Duan Yufeng1, Zhu Wenjing2, Chen Qiao1, Cui Hong3
1 Business School, East China Normal University, Shanghai 200241, China;
2 Institute of Scientific and Technical Information of Shanghai, Shanghai Library, Shanghai 200031, China;
3 School of Information Resources and Library Science, University of Arizona, Tucson, AZ85719, USA
[Objective] To reduce cost of machine learning by declining the size of learning dataset in species description text annotation in Chinese. [Methods] Based on Bootstrapping method, design a weakly supervised learning method which performs learning and tagging processes iteratively with a small amount of data at the beginning. The iteration process promotes annotation ability continuously by expanding the knowledge base. [Results] The average score of F-value runs up to 0.911 2 on a dataset with 15 041 sentences. [Limitations] The annotation efficiency might be relatively low on sparse data. [Conclusions] The experimental data shows that the algorithm in this study not only declines the dataset size requirement of machine learning dramatically, but also increases annotation efficiency.
段宇锋, 朱雯晶, 陈巧, 崔红. 朴素贝叶斯算法与Bootstrapping方法相结合的中文物种描述文本语义标注研究*[J]. 现代图书情报技术, 2014, 30(5): 83-89.
Duan Yufeng, Zhu Wenjing, Chen Qiao, Cui Hong. Semantic Annotation of Species Description Text in Chinese by Combining Naïve Bayes Algorithm with Bootstrapping Method. New Technology of Library and Information Service, DOI：10.11925/infotech.1003-3513.2014.05.11.
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