融合TextRank算法的中文短文本相似度计算

doi:10.16180/j.cnki.issn1007-7820.2020.10.009

Abstract

Abstract:

The traditional VSM vector space model often ignores text semantics, and the constructes text feature matrix is sparse. Based on the word vector technology of deep learning, this paper proposes a similarity calculation method that integrates the improved TextRank algorithm. This method uses the word vector embedding technology to build a text vector space, which makes the vector space model possess the semantic relevance. At the same time, with the improved TextRank algorithm to extract text keywords, the expression of text feature is enhanced and a large amount of redundant information is eliminated. The text characteristic of sparse matrix is reduced, which makes the text similarity computing more efficient. The results of the simulation experiments of different models show that the fusion of the improved TextRank algorithm with Bert word vector technology have better performance of text similarity calculation.

Key words: text similarity, extraction, TextRank slgorithm, Bert, word vector technique, vector space model

CLC Number:

TP391

LU Jiawei,CHEN Wei,YIN Zhong. Chinese Short Text Similarity Calculation Based on TextRank Algorithm[J].Electronic Science and Technology, 2020, 33(10): 51-56.

Figures/Tables 9

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References 16

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	TI+cos	TI+em	TI+mean	TR+cos	TR+em	TR+mean	Imp-TR+cos	Imp-TR+em	Imp-TR+mean
6	0.589	0.378	0.571	0.720	0.347	0.728	0.821	0.491	0.824
7	0.607	0.438	0.619	0.795	0.444	0.810	0.857	0.596	0.863
8	0.630	0.498	0.634	0.848	0.545	0.846	0.871	0.682	0.877
9	0.638	0.552	0.647	0.886	0.671	0.879	0.891	0.775	0.889
10	0.649	0.588	0.652	0.888	0.792	0.894	0.904	0.836	0.903
11	0.661	0.600	0.661	0.885	0.835	0.882	0.892	0.868	0.883
12	0.656	0.619	0.661	0.885	0.871	0.864	0.893	0.903	0.879
13	0.662	0.643	0.661	0.881	0.877	0.853	0.878	0.902	0.845
14	0.660	0.648	0.662	0.878	0.887	0.849	0.865	0.900	0.838
15	0.661	0.659	0.661	0.86	0.881	0.826	0.860	0.897	0.829

	P	R	F₁
TF-IDF+cos	1.000	0.557	0.716
TF-IDF+em	0.660	1.000	0.795
W2v+cos	1.000	0.549	0.709
W2+em	1.000	0.543	0.704
W2v+mean	1.000	0.509	0.675
W2v+cos+TR(10)	0.895	0.882	0.888
W2v+em+TR(14)	0.920	0.856	0.887
W2v+mean+TR(10)	0.930	0.861	0.894
W2v+cos+TI(13)	0.975	0.501	0.662
W2v+em+TI(15)	0.965	0.500	0.659
W2v+mean+TI(14)	0.980	0.500	0.662
W2v+cos+Imp-TR(10)	0.960	0.853	0.904
W2v+em+Imp-TR(12)	0.885	0.922	0.903
W2v+mean+Imp-TR(10)	0.975	0.841	0.903

	P	R	F₁
W2v+cos+TR	0.873	0.867	0.870
W2v+em+TR	0.795	0.774	0.785
W2v+mean+TR	0.634	0.986	0.772
W2v+cos+TI	0.824	0.917	0.868
W2v+em+TI	0.683	0.889	0.772
W2v+mean+TI	0.584	0.989	0.735
W2v+cos+Imp-TR	0.878	0.881	0.880
W2v+em+Imp-TR	0.784	0.853	0.817
W2v+mean+Imp-TR	0.650	0.995	0.787

	P	R	F₁
BERT+cos+TR	0.921	0.917	0.919
BERT+em+TR	0.944	0.785	0.857
BERT+mean+TR	0.967	0.793	0.871
BERT+cos+TI	0.930	0.852	0.889
BERT+em+TI	0.689	0.995	0.814
BERT+mean+TI	0.974	0.824	0.893
BERT+cos+Imp-TR	0.891	0.978	0.932
BERT+em+Imp-TR	0.936	0.867	0.901
BERT+mean+Imp-TR	0.847	0.947	0.894

Chinese Short Text Similarity Calculation Based on TextRank Algorithm

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