基于多属性加权的社会化问答社区关键词提取方法  

Keywords Extraction Method for the Social Q&A Community Based on Multi-attributes Weighted

作　　者：余本功[1,2] 李婷[1] 杨颖[1] 

机构地区：[1]合肥工业大学管理学院,合肥230009 [2]合肥工业大学过程优化与智能决策教育部重点实验室,合肥230009

出　　处：《图书情报工作》2018年第5期132-139,共8页Library and Information Service

基　　金：国家自然科学基金项目“基于制造大数据的产品研发知识集成与服务机制研究”(项目编号:71671057)和“不确定环境下的复杂产品研发协同绩效动态评价研究”(项目编号:71573071)研究成果之一

摘　　要：[目的／意义]现有的关键词提取方法不适应社会化问答社区文本长度较短、内容表述口语化、数据集稀疏的特点，且很少考虑用户关注程度对词语重要性的影响，不能有效地提取此类文本的关键词，因此，提出针对社会化问答社区的多属性加权关键词提取方法。[方法／过程]多属性加权关键词提取方法通过引入调节函数和词性对传统TF-IDF进行改进，并通过线性加权融合用户回答数、关注数、浏览数以及评论数4个用户关注属性来综合度量词语权重。[结果／结论]实验表明，该方法能更有效地提取社会化问答社区文本的关键词。[ Purpose/significance ] Existing methods of extracting keywords can＇ t be applied to the social Q＆A community effectively, because they are not suitable for the characteristics of the social Q＆A community which embodies short texts, colloquial contents and sparse data. They rarely think about the impact of users＇ attention on words. In view of the aforementioned problem, this paper presents a novel keywords extraction method based on multi-attributes weighted for the social Q＆A community. [ Method/process ] This method improved the traditional TF-IDF algorithm by introducing the tuning function and the part of speech. Besides, it calculated the weight of words based on a linear weighting formula, which fused four attributes of user focus by dealing with numbers of users＇ answer, attention, browse, and comments. [ Resuit/conclusion] Experiments show that this method can extract keywords from the social Q＆A community more effectively.

关 键 词：社会化问答社区 关键词提取TF-IDF 多属性加权 

分 类 号：TP391[自动化与计算机技术—计算机应用技术]