PositionRank: An Unsupervised Approach to Keyphrase Extraction from Scholarly Documents

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This paper proposes PositionRank, an unsupervised model for keyphrase extraction from scholarly documents that incorporates information from all positions of a word's occurrences into a biased PageRank.

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11 p.

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Florescu, Corina & Caragea, Cornelia August 2017.

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This article is part of the collection entitled: UNT Scholarly Works and was provided by UNT College of Engineering to Digital Library, a digital repository hosted by the UNT Libraries. More information about this article can be viewed below.

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UNT College of Engineering

The UNT College of Engineering promotes intellectual and scholarly pursuits in the areas of computer science and engineering, preparing innovative leaders in a variety of disciplines. The UNT College of Engineering encourages faculty and students to pursue interdisciplinary research among numerous subjects of study including databases, numerical analysis, game programming, and computer systems architecture.

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This paper proposes PositionRank, an unsupervised model for keyphrase extraction from scholarly documents that incorporates information from all positions of a word's occurrences into a biased PageRank.

Physical Description

11 p.

Notes

Abstract: The large and growing amounts of online scholarly data present both challenges and opportunities to enhance knowledge discovery. One such challenge is to automatically extract a small set of keyphrases from a document that can accurately describe the document’s content and can facilitate fast information processing. In this paper, we propose PositionRank, an unsupervised model for keyphrase extraction from scholarly documents that incorporates information from all positions of a word’s occurrences into a biased PageRank. Our model obtains remarkable improvements in performance over PageRank models that do not take into account word positions as well as over strong baselines for this task. Specifically, on several datasets of research papers, PositionRank achieves improvements as high as 29.09%.

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  • 55th Annual Meeting of the Association for Computational Linguistics, July 30 - August 4, 2017. Vancouver, Canada.

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  • Publication Title: Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics
  • Pages: 1105-1115
  • Peer Reviewed: Yes

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UNT Scholarly Works

Materials from the UNT community's research, creative, and scholarly activities and UNT's Open Access Repository. Access to some items in this collection may be restricted.

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  • August 2017

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  • Aug. 29, 2017, 9:38 a.m.

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Florescu, Corina & Caragea, Cornelia. PositionRank: An Unsupervised Approach to Keyphrase Extraction from Scholarly Documents, article, August 2017; Stroudsburg, Pennsylvania. (digital.library.unt.edu/ark:/67531/metadc990953/: accessed December 13, 2017), University of North Texas Libraries, Digital Library, digital.library.unt.edu; crediting UNT College of Engineering.