KEK：基于k-truss的短文本关键词提取方法

关键词提取在众多文本挖掘任务中扮演着重要的角色,其提取效果直接影响了文本挖掘任务的质量。以文本为研究对象,提出了一种基于k-truss图分解的关键词提取方法,名为KEK(keyword extraction based on k-truss)。该方法首先借助空间向量模型理论,以文本中的词为节点,通过词语之间的共现关系来构建文本图,接着利用k-truss图分解技术来获取文本语义特征,并结合词频、单词位置特征、复杂网络特征等构造无参评分函数,最终根据评分结果来提取关键词。通过在基准数据集上进行实验验证,结果表明KEK算法在提取短文本关键词上的F₁值性能指标优于其他基于文本图的关键词提取方法。

KEK:short text keyword extraction method based on k-truss

Keyword extraction plays an important role in text mining tasks.This paper aimed at proposing a keyword extraction method based on k-truss graph decomposition for short texts,named KEK.Firstly,the method applied the space vector model to present each word in the text,and constructed a text graph by taking words as nodes and the co-occurrence relationship between words as edges.Then,the method used k-truss graph decomposition technology to obtain the semantic features of the text,and considered word frequency,word position,complex network feature to design a parameter less scoring function.Finally,it extracted keyword based on the scoring results.Experiments on real datasets show that KEK performs better than other graph-based keyword extraction methods in terms of F₁score.