一种迭代式的概念属性名称自动获取方法  

An Interative Approach to Automatic Attribute Acquisition

作　　者：汪平仄[1,2] 曹存根[1] 王石[1] 

机构地区：[1]中国科学院计算技术研究所,北京100190 [2]中国科学院大学,北京100049

出　　处：《中文信息学报》2014年第4期58-67,共10页Journal of Chinese Information Processing

基　　金：国家自然科学基金(91224006;61203284;61173063;61035004;30973716);国家社科基金(10AYY003)

摘　　要：属性是一种用于描述概念和鉴别概念的特殊知识。属性名称是表示属性的专有名词。该文提出了一种基于前后缀迭代的方法,从Web网页中获取概念的属性名称。该方法的每一次迭代分为两个阶段:(1)从现有种子属性集中选择合适的前后缀,构造词汇-句法模式,从Web网页中提取候选属性;(2)采用基于相似性的验证模型对候选属性进行验证,以扩充现有属性集合。该文提出了一组验证模型对候选属性进行验证,比较各个模型的优缺点,并在地域类和商业主体类概念上分别得到了平均92.9%和90.7%的准确率,以及对原有种子属性集合近100倍的扩充率。Attributes are a special type of knowledge which is used to describe and identify concepts. Attribute names are proper nouns to express attributes. This paper presents a prefix-- and suffix--based method to extract attrib- utes iteratively from Web pages. In this method, each iteration consists of two phases. （1） Selecting a set of appro- priate attribute prefixes and suffixes from the existing attribute seeds, and generating lexico--syntactic patterns to extract candidate attributes from Web pages. （2） Using a similarity--based model to validate candidate attributes to expand the existing set of seed attributes. We propose a group of validation models, and then compare the advanta- ges and disadvantages of each model. We evaluate our method on a group of concepts in the geographic class and business class. Comprehensive experiments show that an average of 92.9% and 90.7% precision are obtained, re- spectively, and the original set of seed attributes are expanded nearly＇ 100 times.

关 键 词：概念 属性 属性前缀 属性后缀 属性元 知识获取 

分 类 号：TP391[自动化与计算机技术—计算机应用技术]