中文    English

›› 2016, Vol. 28 ›› Issue (3): 18-22.doi: 10.13998/j.cnki.issn1002-1248.2016.03.004

• Information forum • Previous Articles     Next Articles

Research on Keywords Extracting Method from Logic item in Abstract

WANG Xing-lan   

  1. Library of Chongqing Medical University, Chongqing 400016, China
  • Received:2015-11-12 Online:2016-03-05 Published:2016-03-08

Abstract: The abstract of Medical academic article has a clear structure including objective, methods, results, conclusions. This paper used information entropy and manual screening method to extract keywords from each logic item of abstract, and then ranked the keywords and compared with original keywords. To improve the results of automatic extracting method, it calculated the weight of each item in abstract by using java program and offered a new ranking for the extracted keywords. The optimal experimental results showed that the ratio 7:1:1:4 for logic item could get the best effect of automatic extraction.

Key words: Candidate

CLC Number: 

  • G251
[1] Kim S N, Kan M. Re-examining Automatic Keyphrase Extraction Approaches in Scientific Articles [C]. In: Proceedings of the Workshop on Multiword Expressions: Identification, Interpretation, Disambiguation and Applications (MWE’09). Stroudsburg: Association for Computational Linguistics,2009:9-16.
[2] HaCohen-Kerner Y, Gross Z, Masa A. Automatic Extraction and Learning of Keyphrases from Scientific Articles [C]. In: Proceedings of the 6th International Conference on Computational Linguistics and Intelligent Text Processing (CICLing’05). Berlin,Heidelberg: Springer-Verlag, 2005: 657-669.
[3] Alzahrani S, Palade V, Salim N, et al. Using Structural Information and Citation Evidence to Detect Significant Plagiarism Cases in Scientific Publications [J]. Journal of the American Society for Information Science and Technology,2012,63(2):286-312.
[4] Nguyen T D, Luong M. WINGNUS: Keyphrase Extraction Utilizing Document Logical Structure [C]. In: Proceedings of the 5th International Workshop on Semantic Evaluation (SemEval’10). Stroudsburg: Association for Computational Linguistics,2010:166-169.
[5] 何远标,乐小虬,张帆.学术论文大纲中关键术语抽取方法研究[J].现代图书情报技术,2014,(244):73-79.
[6] 吴胜远.并行分词方法的研究[J].计算机研究与发展,1997,(7):542-545.
[7] Nguyen T D, Luong M. WINGNUS: Keyphrase Extraction Utilizing Document Logical Structure [C]. In: Proceedings of the 5th International Workshop on Semantic Evaluation (SemEval’10). Stroudsburg: Association for Computational Linguistics,2010:166-169.
[8] Lopez C, Prince V, Roche M. Automatic Titling of Electronic Documents with Noun Phrase Extraction [C]. In: Proceedings of 2010 International Conference of Soft Computing and Pattern Recognition (SoCPaR), Paris, France.IEEE,2010:168-171.
[9] Li D, Li S, Li W, et al. A Semi-supervised Key Phrase Extraction Approach: Learning from Title Phrases Through a Document Semantic Network [C]. In: Proceedings of the ACL 2010 Conference Short Papers. Stroudsburg: Association for Computational Linguistics, 2010:296-300.
[10] Liao L, Huang H. Microblog Keyphrase Extraction Based on Similarity Features[C]. In: Proceedings of 2013 International Conference on Advanced Computer Science and Electronics Information (ICACSEI’13).2013.
[11] 利用信息熵提取文章关键词[EB/OL].[2015-10-15].http://blog.csdn.net/zhaoxinfan/article/details/12751405.
[12] NLPIR中文分词系统[EB/OL].[2015-10-15].http://ictclas.nlpir.org/.
Viewed
Full text


Abstract

Cited

  Shared   
  Discussed   
No Suggested Reading articles found!