摘要
针对相似话题难以区分的问题,提出了基于层叠模型的话题检测方法.该方法以Single-Pass聚类策略为基础,将新闻实体信息运用到话题检测中,改进时间相似度和地点相似度的计算方法,在底层利用文本内容相似度完成话题检测的任务,在高层结合时间相似度和地点相似度完成话题检测的任务.实验结果表明,该方法的性能优于传统的文本相似度算法.
A novel approach was proposed based on the cascade model for the topic detection to effective- ly distinguish similar topics. Based on Single-Pass clustering algorithm, the entity information was used in topic detection, two similarity methods were proposed, including time-based similarity and location-based similarity. Moreover, at the bottom level of the system, content-based similarity was used to realize topic detection task. At the top level of the system, three similarity results were effectively combined to realize topic detection task. Experimental results showed that the performance of this novel algorithm was superi- or to the traditional text similarity algorithm.
出处
《郑州大学学报（理学版）》
CAS
北大核心
2012年第2期43-47,共5页
Journal of Zhengzhou University:Natural Science Edition
关键词
话题检测
相似话题
向量空间模型
层叠模型
topic detection
similar topic
vector space model（VSM）
cascade model
