基于多算法多因素融合的关键词提取方法

Chinese Text Keyword Extraction Method Based on Multi-Factor Fusion

为进一步提升关键词提取准确率,提出基于多算法多特征融合的中文文本关键词提取方法。对现有的TF-IDF算法和TextRank算法进行融合,同时融合词频、词长、词语位置、词性这四种影响因素进行加权。通过试验获取权重公式的相对最优权重系数,对改进后两种算法分别提取出权重值由高到低的前K个候选关键词,最终采取投票法筛选出结果。从准确率P、召回率R、准确率和召回率的加权调和平均值F值三个指标对文中融合改进算法(Fusion-T算法)、经典的TF-IDF算法和TextRank算法进行对比。试验结果表明:算法改进后,P、R、F值分别提高了:6.18%、4.97%、5.99%。

In order to further improve the accuracy of keyword extraction,a Chinese-text keyword extraction method based on multi-algorithm and multi-feature fusion is proposed.The existing TF-IDF algorithm and TextRank algorithm are fused,and the four influencing factors of word frequency,word length,word position,and part-of-speech are combined to weight.The relative optimal weight coefficient of the weight formula was obtained through experiments,and the top K candidate keywords with weight values from high to low were extracted for the improved two algorithms,and finally the results were filtered by voting.The three indexes of P(accuracy),R(recall)and F value(weighted harmonic average of P and R)were compared.Experimental results show that after the algorithm is improved,the values of P,R and F increase by 6.18%,4.97%and 5.99%,respectively.