词共现网络的遗传聚类在话题发现中的应用  

Application of GCA of word co-occurrence network in topic detection

作　　者：杨菲[1] 黄柏雄[2] 

机构地区：[1]珠海城市职业技术学院工程与信息学院,广东珠海519090 [2]广西大学计算机与电子信息学院,南宁530004

出　　处：《计算机工程与应用》2013年第14期126-129,146,共5页Computer Engineering and Applications

基　　金：广西教育厅科研基金项目(No.201012MS010)

摘　　要：基于词聚类的话题发现方法中,普遍存在聚类结果不稳定(聚类结果较大程度依赖于聚类对象的初始化操作)的问题,为此通过将文档集建模为词共现网络,设计词共现网络的过滤方法,然后提出基于词共现网络的遗传聚类算法,实现从网络文档中提取热点话题。与已有方法相比,该方法所发现的话题相对稳定,这在实验中亦得到了验证,因而该方法在实际应用中具有更好的现实意义。In the topic detection methods, there usually exists the problem of unstable clustering results. In this paper, a network document set is modeled as word co-occurrence network, and a filtering method is designed so as to simplify the network, and then a GCA （Genetic Clustering Algorithm） is proposed for clustering the simplified network, such extracting topics from a net- work document set. Compared with other existing methods, the proposed method seems more stable for the obtained clustering results, which also has been confirmed in the experiment. This means the proposed method has better practical significance in actual applications.

关 键 词：话题发现 词共现网络 遗传聚类算法 词聚类算法 

分 类 号：TP391[自动化与计算机技术—计算机应用技术]