Journal of Information Processing Systems

Korea Information Processing Society (한국정보처리학회)
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A Dependency Graph-Based Keyphrase Extraction Method Using Anti-patterns

  • Batsuren, Khuyagbaatar (Doctoral School of Information and Communication Technology, University of Trento) ;
  • Batbaatar, Erdenebileg (Database and Bioinformatics Laboratory, School of Electrical and Computer Engineering, Chungbuk National University) ;
  • Munkhdalai, Tsendsuren (Dept. of Quantitative Health Sciences University, University of Massachusetts Medical School) ;
  • Li, Meijing (College of Information Engineering, Shanghai Maritime University) ;
  • Namsrai, Oyun-Erdene (School of Engineering and Applied Science, National University of Mongolia) ;
  • Ryu, Keun Ho (Database and Bioinformatics Laboratory, School of Electrical and Computer Engineering, Chungbuk National University)
  • Received : 2015.04.07
  • Accepted : 2017.02.09
  • Published : 2018.10.31
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Abstract

Keyphrase extraction is one of fundamental natural language processing (NLP) tools to improve many text-mining applications such as document summarization and clustering. In this paper, we propose to use two novel techniques on the top of the state-of-the-art keyphrase extraction methods. First is the anti-patterns that aim to recognize non-keyphrase candidates. The state-of-the-art methods often used the rich feature set to identify keyphrases while those rich feature set cover only some of all keyphrases because keyphrases share very few similar patterns and stylistic features while non-keyphrase candidates often share many similar patterns and stylistic features. Second one is to use the dependency graph instead of the word co-occurrence graph that could not connect two words that are syntactically related and placed far from each other in a sentence while the dependency graph can do so. In experiments, we have compared the performances with different settings of the graphs (co-occurrence and dependency), and with the existing method results. Finally, we discovered that the combination method of dependency graph and anti-patterns outperform the state-of-the-art performances.

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References

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