Keyphrase Extraction Using Sequential Patterns Mining Algorithm with One-Off and General Gaps Condition

被引：0
|
作者：
Liu H.-T. [1 ,2 ]
Liu Z.-Z. [1 ,2 ]
Wang L.-L. [1 ,2 ]
Wu X.-D. [3 ,4 ]
机构：
[1] Key Laboratory of Intelligent Computing and Signal Processing of the Ministry of Education, Anhui University, Hefei, 230039, Anhui
[2] School of Computer Science and Technology, Anhui University, Hefei, 230601, Anhui
[3] School of Computer Science and Information Engineering, Hefei University of Technology, Hefei, 230009, Anhui
[4] School of Computing and Informatics, University of Louisiana at Lafayette, Lafayette
来源：
Tien Tzu Hsueh Pao/Acta Electronica Sinica | 2019年 / 47卷 / 05期
关键词：
General gap; Keyphrase extraction; Machine learning; Sequential patterns mining;
D O I：
10.3969/j.issn.0372-2112.2019.05.020
中图分类号：
学科分类号：
摘要：
Keyphrases are used to summarize the document and high-quality keyphrases have great importance in text summarizing, reading and indexing. However, most studies of keyphrase extraction have strict limitation in the form of patterns, and are unable to achieve the semantic relation between words and phrases. The results are failure to autonomously extract keyphrases. Keyphrase extraction using sequential patterns mining with one-off and general gaps condition algorithm (KEING) is proposed in this paper. Taking into account one off condition and general gaps, SPING(Sequential Patterns mIning with oNe-off and General gaps condition)can catch semantic relations between words and phrases more effectively. Therefore, KEING will get effective candidate keyphrases and count their features. Then a supervised machine learning method is used to train features and construct a classification model, we can extract keyphrase with this model. Experimental results demonstrate KEING can effectively extract high quality keyphrases. © 2019, Chinese Institute of Electronics. All right reserved.
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收藏
页码：1121 / 1128
页数：7
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