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Data Analysis and Knowledge Discovery  2019, Vol. 3 Issue (8): 68-76    DOI: 10.11925/infotech.2096-3467.2018.0765
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Extracting Keywords Based on Topic Structure and Word Diagram Iteration
Mingzhu Sun,Jing Ma(),Lingfei Qian
School of Economics and Management, Nanjing University of Aeronautics and Astronautics, Nanjing 210016, China
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Abstract  

[Objective] This paper integrates the topic information to the TextRank model, aiming to improve the precision and recall of automatic keyword extraction. [Methods] First, we used the LDA to create a model for document topics, and obtained the topic distribution of the candidate keywords. Then, we calculated the node weights with the topic-word probability distribution features. Third, we weighted the probability distributions of document-topic and topic-word characteristics as the node’s random jump probability. Finally, we constructed a new transition matrix for word graph iteration to improve the TextRank model. [Results] We examined the proposed model with 1559 news articles from the website of Southern Weekly. When the number of extracted keywords was three, the model’s keyword extraction precision values were 4.7% and 6.5% higher than those of the original TextRank and TF-IDF algorithms. [Limitations] The fusion algorithm increased computational complexity. [Conclusions] The proposed algorithm could extract keywords more effectively.

Key wordsKeywords Extraction      TextRank      Latent Dirichlet Allocation      Graph Model     
Received: 15 July 2018      Published: 29 September 2019
ZTFLH:  TP393 G35  
Corresponding Authors: Jing Ma     E-mail: majing5525@126.com

Cite this article:

Mingzhu Sun,Jing Ma,Lingfei Qian. Extracting Keywords Based on Topic Structure and Word Diagram Iteration. Data Analysis and Knowledge Discovery, 2019, 3(8): 68-76.

URL:

http://manu44.magtech.com.cn/Jwk_infotech_wk3/EN/10.11925/infotech.2096-3467.2018.0765     OR     http://manu44.magtech.com.cn/Jwk_infotech_wk3/EN/Y2019/V3/I8/68

K P R F1
50 0.248 0.209 0.224
100 0.251 0.211 0.226
150 0.252 0.212 0.228
200 0.250 0.211 0.226
TopN TF-IDF TextRank LDA 文献[13]方法 文献[14]方法 本文算法
P R F1 P R F1 P R F1 P R F1 P R F1 P R F1
3 0.213 0.182 0.196 0.231 0.194 0.211 0.243 0.203 0.221 0.245 0.206 0.224 0.248 0.211 0.228 0.278 0.239 0.257
5 0.163 0.23 0.191 0.175 0.244 0.204 0.191 0.256 0.219 0.203 0.256 0.226 0.213 0.282 0.243 0.216 0.289 0.247
7 0.135 0.264 0.179 0.141 0.274 0.186 0.162 0.289 0.208 0.169 0.293 0.214 0.183 0.325 0.234 0.181 0.323 0.232
9 0.116 0.291 0.166 0.12 0.299 0.171 0.135 0.318 0.190 0.145 0.324 0.200 0.162 0.357 0.223 0.159 0.351 0.219
15 0.083 0.343 0.134 0.083 0.344 0.134 0.102 0.362 0.159 0.106 0.375 0.165 0.124 0.411 0.191 0.119 0.399 0.183
文档编号 抽取方法 关键词
1 2 3 4 5
1 TextRank 宜昌 郭有明 部门 透露 知情人
本文算法 郭有明 副省长 报道 涉嫌 违纪违法
2 TextRank 幼儿 装修 幼儿园 咳嗽 皮肤
本文算法 幼儿园 甲醛 装修 咳嗽 过敏
3 TextRank 公司 丁羽心 人民币, 刘志军 有限公司
本文算法 丁羽心 刘志军 并处 有限公司, 非法经营
4 TextRank HPV疫苗 宫颈癌 接种 默沙东 试验
本文算法 HPV疫苗 宫颈癌 临床试验 中国 上市
5 TextRank 报道 衡阳市 破坏选举 衡阳 人大代表
本文算法 破坏选举 衡阳 人大代表 涉嫌 立案
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