基于多重增强图和主题分析的社交短文本检索方法

摘要

社会网络平台上的社交短文本不同于网页或其他文本,它的特点是内容短、文本间存在转发评论等关系、话题复杂多样、与Web页面有链接关系、文本的作者间有关注关系等,现有的检索系统不能完全适应.该文提出一个基于多重增强图的社交短文本检索方法SSTR,它利用多重增强图算法对通过Indri获得的初步检索结果实现再排序优化和去重.多重增强图算法是基于马尔科夫链理论设计出的图模型算法,社交短文本中蕴含的文本、作者、词语等不同层面的关系通过不同的图层及图中节点之间的边来建模.三个层面的关系相互增强,通过多次迭代运算,最终寻求多个层面间相互关系所处的稳定状态.多重增强图构建时,短文本的相似度计算基于主题分析结果,克服了传统余弦相似度计算时 T F-IDF权重在短文本上的局限性.实验结果表明,与Indri、reRank-COS和reRank-LDA相比,基于多重增强图算法的SSTR排序的效果更好,适合初始检索结果相对较多的应用场合.%Social short texts,coming from Twitter,Sina Microblog,etc.,are limited in length but bear diversified topics,complex social relationships,as well as strong correlation with Web pages.Therefore,the traditional infor-mation retrieval methods are not suitable for the socialized short texts.The paper proposes a social short text re-trieval method,SSTR,based on multiple-enhanced graph.The multiple-enhanced graph algorithm is based on Markov chain theory,where three types of relationships between short texts,authors,and tokens are considered. In SSTR,topic model based on LDA is employed when computing the similarity between short texts,which could overcome the disadvantages of T F-IDF feature.Experimental results show that,compared to cosine similarity based and LDA based re-ranking,SSTR produces better re-ranking result.