传统的文本聚类往往采用词包模型构建文本向量,忽略了词语间丰富的语义信息。而基于中心划分的聚类算法,容易将概念相关的自然簇强制分开,不能很好地发现人们感兴趣的话题。该文针对传统文本聚类算法的缺点,提出一种基于语义和完全子图的短文本聚类算法,通过对目前主流的三大语义模型进行了实验和对比,选择了一种较为先进的语义模型,基于该语义模型进行了聚类实验,发现新算法能较好地挖掘句子的语义信息且较传统的K-means有更高的聚类纯度。
Abstract
Traditional methods for text clustering have generally taken the BOW (bag-of-words) model to construct the vector of document, ignoring semantic information between words. And partitioning clustering method based on centroid tends to split concept closely related clusters stiffly, not suitable for mining interesting topics. To address these issues, , this paper proposes a text clustering method based on semantics and cliques. Compared with three popular semantic models, experiments reveal that our method performs better than K-means on semantic clustering task.
Keywords: text clustering method;complete sub-graph;semantic similarity;distributed representations of words in a vector space 收稿日期: 2015-04-07 定稿日期: 2015-06-02 基金项目: 海量网络数据流海云协同实时处理系统(子课题)(XDA06011203);电视商务综合体新业态运营支撑系统开发(2012BAH73F01)
关键词
文本聚类 /
完全子图 /
语义相似度 /
词向量
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参考文献
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脚注
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基金
海量网络数据流海云协同实时处理系统(子课题)(XDA06011203);电视商务综合体新业态运营支撑系统开发(2012BAH73F01)
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