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Data Analysis and Knowledge Discovery  2019, Vol. 3 Issue (11): 35-44    DOI: 10.11925/infotech.2096-3467.2019.0143
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Extracting Keywords Based on Removed Network Word Nodes
An Wang,Yijun Gu(),Kunming Li,Wenzheng Li
College of Information Technology and Cyber Security, People’s Public Security University of China, Beijing 102600, China
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Abstract  

[Objective] This study modifies the TextRank algorithm with a method of removing word nodes, aiming to improve the results of keyword extraction from Chinese documents. [Methods] We proposed an updated RemoveRank algorithm to collect Chinese keywords and alternately carried out the sorting and removing steps. Based on the complex network structure characteristics of word graph, we used the removal queue as the sorting results for word nodes to extract keywords. [Results] We examined the proposed method on dataset with marked keywords from Southern Weekend. The new algorithm had better performance than the traditional methods. When the number of extracted keywords were 3, 5, and 7, their F values were 4%, 6%, and 5% higher than those of the TextRank. [Limitations] Our word graph did not include the weight of edges. [Conclusions] The RemoveRank method could effectively extract keywords from Chinese documents with the appropriate sliding window values.

Key wordsExtraction      TextRank      Graph Model      Word Node      Sub-Graph Partitioning     
Received: 31 January 2019      Published: 18 December 2019
ZTFLH:  TP391  
Corresponding Authors: Yijun Gu     E-mail: guyijun@ppsuc.edu.cn

Cite this article:

An Wang,Yijun Gu,Kunming Li,Wenzheng Li. Extracting Keywords Based on Removed Network Word Nodes. Data Analysis and Knowledge Discovery, 2019, 3(11): 35-44.

URL:

https://manu44.magtech.com.cn/Jwk_infotech_wk3/EN/10.11925/infotech.2096-3467.2019.0143     OR     https://manu44.magtech.com.cn/Jwk_infotech_wk3/EN/Y2019/V3/I11/35

统计指标 大小
文档总数 1 027
文档平均词节点数 336
文档平均句子数 73
文档标注关键词数 3.6
滑动窗口 聚类系数 P R F
2 0.033 0.275 0.382 0.320
3 0.500 0.287 0.398 0.333
4 0.632 0.292 0.405 0.339
5 0.693 0.289 0.401 0.336
6 0.730 0.288 0.399 0.334
7 0.756 0.287 0.398 0.334
8 0.776 0.286 0.397 0.332
9 0.792 0.286 0.398 0.333
10 0.806 0.287 0.398 0.333
抽取个数 方法 P R F
3 TextRank 0.366 0.304 0.332
TF-IDF 0.376 0.313 0.342
中间中心性(BC) 0.356 0.296 0.323
接近中心性(CC) 0.337 0.281 0.306
MixRank 0.374 0.311 0.339
RemoveRank 0.382 0.318 0.347
5 TextRank 0.273 0..379 0.318
TF-IDF 0.274 0.380 0.319
中间中心性(BC) 0.262 0.364 0.305
接近中心性(CC) 0.246 0.341 0.286
MixRank 0.274 0.381 0.319
RemoveRank 0.291 0.405 0.339
7 TextRank 0.215 0.418 0.284
TF-IDF 0.219 0.425 0.289
中间中心性(BC) 0.207 0.403 0.274
接近中心性(CC) 0.197 0.383 0.260
MixRank 0.215 0.418 0.284
RemoveRank 0.226 0.439 0.298
文档 方法 标注关键词 抽取关键词
6 TextRank 宫颈癌, hpv, 上市, 疫苗 疫苗, 宫颈癌, 中国, hpv, 接种
RemoveRank 宫颈癌, hpv, 上市, 疫苗 疫苗, hpv, 宫颈癌, 中国, 上市
1008 TextRank 互联网, 光缆, 服务器 互联网, 连接, 服务器, 网站, 网络
RemoveRank 互联网, 光缆, 服务器 互联网, 光缆, 服务器, 网站, 连接
1364 TextRank 青海湖, 塔尔寺, 油菜花, 牦牛 油菜花, 青海湖, 黄教, 牦牛, 之称
RemoveRank 青海湖, 塔尔寺, 油菜花, 牦牛 油菜花, 青海湖, 塔尔寺, 牦牛, 黄教
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