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

LIU Hui-ting; LIU Zhi-zhong; WANG Li-li; WU Xin-dong

doi:10.3969/j.issn.0372-2112.2019.05.020

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

更新时间：2025-07-16

- Keyphrase Extraction Using Sequential Patterns Mining Algorithm with One-Off and General Gaps Condition
- Acta Electronica Sinica Vol. 47, Issue 5, Pages: 1121-1128(2019)
- 作者机构：
  
  1. 安徽大学计算智能与信号处理教育部重点实验室,安徽,合肥,230601
  2. 安徽大学计算机科学与技术学院,安徽,合肥,230601
  3. 合肥工业大学计算机与信息学院,安徽,合肥,230601
  4. School of Computing and Informatics, University of Louisiana at Lafayette, Lafayette,70503
  5. 安徽大学计算智能与信号处理教育部重点实验室,安徽,合肥,230601
  6. 安徽大学计算机科学与技术学院,安徽,合肥,230601
  7. 合肥工业大学计算机与信息学院,安徽,合肥,230601
  8. School of Computing and Informatics University of Louisiana at Lafayette Lafayette,70503
- 作者简介：
- 基金信息：
  
  National Key Research and Development Program of China (No.2016YFB1000901);National Natural Science Foundation of China (No.61202227);Natural Science Research Program of Colleges and Universities of Anhui Province (No.KJ2018A0013)
- DOI：10.3969/j.issn.0372-2112.2019.05.020
  CLC： TP181
- Published Online：25 May 2019，
  
  Published：2019
- 稿件说明：
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LIU Hui-ting, LIU Zhi-zhong, WANG Li-li, et al. Keyphrase Extraction Using Sequential Patterns Mining Algorithm with One-Off and General Gaps Condition[J]. Acta Electronica Sinica, 2019, 47(5): 1121-1128.
DOI：

LIU Hui-ting, LIU Zhi-zhong, WANG Li-li, et al. Keyphrase Extraction Using Sequential Patterns Mining Algorithm with One-Off and General Gaps Condition[J]. Acta Electronica Sinica, 2019, 47(5): 1121-1128. DOI： 10.3969/j.issn.0372-2112.2019.05.020.

摘要

本文提出了有监督的关键词抽取算法KEING（Keyphrase Extraction using sequentIal patterns with oNe-off and General gaps condition）算法.首先，将每篇文档作为一个序列库，利用SPING（Sequential Patterns mIning with oNe-off and General gaps condition）算法获取词语之间的关系及其多种变化形式，并利用统计模式特征的方式描述候选关键词；然后，通过朴素贝叶斯分类算法对大量带标记的训练数据进行训练，构造分类器；最后利用分类器从测试文档中识别出关键词.通过实验验证了SPING算法的完备性以及KEING算法的有效性.

Abstract

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.

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