Citation-Enhanced Keyphrase Extraction from Research Papers: A Supervised Approach

PDF Version Also Available for Download.

Description

This paper proposes a supervised model for keyphrase extraction from research papers, which are embedded in citation networks.

Physical Description

12 p.

Creation Information

Caragea, Cornelia; Bulgarov, Florin; Godea, Andreea & Das Gollapalli, Sujatha October 2014.

Context

This paper 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 paper can be viewed below.

Who

People and organizations associated with either the creation of this paper or its content.

Authors

Publisher

Provided By

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.

Contact Us

What

Descriptive information to help identify this paper. Follow the links below to find similar items on the Digital Library.

Description

This paper proposes a supervised model for keyphrase extraction from research papers, which are embedded in citation networks.

Physical Description

12 p.

Notes

Abstract: Given the large amounts of online textual documents available these days, e.g., news articles, weblogs, and scientific papers, effective methods for extracting keyphrases, which provide a high-level topic description of a document, are greatly needed. In this paper, we propose a supervised model for keyphrase extraction from research papers, which are embedded in citation networks. To this end, we design novel features based on citation network information and use them in conjunction with traditional features for keyphrase extraction to obtain remarkable improvements in performance over strong baselines.

Source

  • 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP), October 25-29, 2014. Doha, Qatar.

Language

Item Type

Publication Information

  • Publication Title: Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP)
  • Pages: 1435-1446
  • Peer Reviewed: Yes

Collections

This paper is part of the following collection of related materials.

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.

What responsibilities do I have when using this paper?

When

Dates and time periods associated with this paper.

Creation Date

  • October 2014

Added to The UNT Digital Library

  • Aug. 29, 2017, 9:38 a.m.

Usage Statistics

When was this paper last used?

Yesterday: 0
Past 30 days: 0
Total Uses: 1

Interact With This Paper

Here are some suggestions for what to do next.

Start Reading

PDF Version Also Available for Download.

Citations, Rights, Re-Use

Caragea, Cornelia; Bulgarov, Florin; Godea, Andreea & Das Gollapalli, Sujatha. Citation-Enhanced Keyphrase Extraction from Research Papers: A Supervised Approach, paper, October 2014; Stroudsburg, Pennsylvania. (digital.library.unt.edu/ark:/67531/metadc991010/: accessed October 20, 2017), University of North Texas Libraries, Digital Library, digital.library.unt.edu; crediting UNT College of Engineering.