茶学本体学习中的概念抽取①

提出了一种基于茶学词典和统计算法相结合的茶学知识概念抽取方法。该方法以茶学词典为基础，首先对非结构化数据源进行中文分词处理，然后采用两种统计算法对分词结果进行概念抽取。通过使用丰富的茶学词典来降低统计算法时间复杂度，提高了中文分词和概念抽取的精度和效率。实验结果表明，词库的丰富程度决定了概念抽取的效果，可以通过不断丰富词库，进一步提高概念抽取精度。

Concept Extraction in Tea Ontology Learning

A concept extraction method is presented based on tea dictionary and statistics. The method takes tea dictionary as basis. Firstly, unstructured data source is in Chinese word segment processing, and then, two statistics algorithms are applied to extracted tea concept from Chinese segment results. The approach improves the precision and efficiency of Chinese segment and concept extraction by reducing the time complexity of statistical algorithms with the rich tea dictionary. The experimental results show that the degree of dictionary richness determines the efficiency of tea concept extraction, and can be improved by updating tea dictionary.