基于粗糙数据推理的TextRank关键词提取算法

基于图模型的TextRank算法是一种有效的关键词提取算法,在提取关键词时可取得较高准确度。但该算法在构造图的关联边时,所采用的共现窗口规则仅考虑了局部词汇间的关联,并具有较大随意性与不确定性。针对这一问题,该文提出了一种基于粗糙数据推理理论的改进TextRank关键词提取算法,粗糙数据推理可扩大关联范围,增加关联数据,得到的结果更加全面。结合粗糙数据推理理论中的关联规则,该文提出的算法做了以下改进: 依据词义对候选关键词进行划分;再通过粗糙数据推理对不同分类中候选词间的关联关系进行推理。实验结果表明,与传统的TextRank算法相比,改进后算法的提取精度有了明显的提高,证明了利用粗糙数据推理的思想能有效地改善算法提取关键词的性能。

TextRank Keyword Extraction Algorithm Based on Rough Data-Deduction

TextRank algorithm based on graph model is an effective keyword extraction algorithm with high accuracy. However, when constructing the edges of a graph, the algorithm adopts the co-occurrence window rule that considers only the association between local words, yielding greater randomness and uncertainty. To address the issue, an improved TextRank keyword extraction algorithm based on rough data-deduction is proposed. In this method, candidate keywords are classified according to word meanings, and the association between candidate words in different classes is deduced by rough data-deduction. The experimental results show that the extraction precision of improved algorithm has been significantly improved.