摘要： 结合通配符模式与引入先验信息的随机游走算法，提出一种改进的关键词提取方法。使用通配符约束捕获词语之间的语义关系，提取满足间隙约束和一次性条件的顺序模式以计算模式支持度，并在模式支持度大于等于最小支持度阈值时建立节点关联图。将维基百科知识库中词语间的相似度作为先验信息，利用基于先验信息的PageRank算法在关联图上进行随机游走直至其排名分数趋于稳定，选取排名前Top K个词语作为关键词。实验结果表明，与TextRank、GraphSum算法相比，该方法具有更高的提取准确率及稳定性。

关键词: 关键词提取, 通配符模式, 随机游走, 间隙约束, PageRank算法

Abstract: Based on the wildcard patterns and the random walk algorithm with prior information,this paper proposes an improved keyword extraction algorithm.The algorithm uses wildcard constraint to capture the semantic information between words,and extracts the sequential pattern that satisfies the gap constraint and the one-time condition in order to calculate the pattern support degree.When the pattern support degree is not lower than the threshold of minimum support degree,the node association graph is established.The similarity between words in the Wikipedia knowledge base is taken as priori information,and random walks are performed on the association graph by using the PageRank algorithm based on priori information,until the ranking scores stabilize.The Top K words are selected as keywords.Experimental results show that the proposed method has higher extraction accuracy and stability than TextRank,GraphSum and other algorithms.

Key words: keyword extraction, wildcard pattern, random walk, gap constraint, PageRank algorithm

中图分类号: 

• TP18