A Position-Biased PageRank Algorithm for Keyphrase Extraction

AAAI 2017 pp. 4923-4924

doi:10.1609/AAAI.V31I1.11082 /aaai/2017/florescu2017aaai-position/

Abstract

Given the large amounts of online textual documents available these days, e.g., news articles and scientific papers, effective methods for extracting keyphrases, which provide a high-level topic description of a document, are greatly needed.We propose PositionRank, an unsupervised graph-based approach to keyphrase extraction that incorporates information from all positions of a word's occurrences into a biased PageRank to extract keyphrases. Our model obtains remarkable improvements in performance over strong baselines.

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Cite

Text

Florescu and Caragea. "A Position-Biased PageRank Algorithm for Keyphrase Extraction." AAAI Conference on Artificial Intelligence, 2017. doi:10.1609/AAAI.V31I1.11082

Markdown

[Florescu and Caragea. "A Position-Biased PageRank Algorithm for Keyphrase Extraction." AAAI Conference on Artificial Intelligence, 2017.](https://mlanthology.org/aaai/2017/florescu2017aaai-position/) doi:10.1609/AAAI.V31I1.11082

BibTeX

@inproceedings{florescu2017aaai-position,
  title     = {{A Position-Biased PageRank Algorithm for Keyphrase Extraction}},
  author    = {Florescu, Corina and Caragea, Cornelia},
  booktitle = {AAAI Conference on Artificial Intelligence},
  year      = {2017},
  pages     = {4923-4924},
  doi       = {10.1609/AAAI.V31I1.11082},
  url       = {https://mlanthology.org/aaai/2017/florescu2017aaai-position/}
}