High-Precision Extraction of Emerging Concepts from Scientific Literature

@article{King2020HighPrecisionEO,
  title={High-Precision Extraction of Emerging Concepts from Scientific Literature},
  author={Daniel King and Doug Downey and Daniel S. Weld},
  journal={Proceedings of the 43rd International ACM SIGIR Conference on Research and Development in Information Retrieval},
  year={2020}
}
  • Daniel King, Doug Downey, Daniel S. Weld
  • Published 2020
  • Computer Science
  • Proceedings of the 43rd International ACM SIGIR Conference on Research and Development in Information Retrieval
Identification of new concepts in scientific literature can help power faceted search, scientific trend analysis, knowledge-base construction, and more, but current methods are lacking. Manual identification can't keep up with the torrent of new publications, while the precision of existing automatic techniques is too low for many applications. We present an unsupervised concept extraction method for scientific literature that achieves much higher precision than previous work. Our approach… Expand

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References

SHOWING 1-10 OF 10 REFERENCES
TSE-NER: An Iterative Approach for Long-Tail Entity Extraction in Scientific Publications
A frequent keyword-set based algorithm for topic modeling and clustering of research papers
Detecting research topics via the correlation between graphs and texts
Phrases as subtopical concepts in scholarly text
Bursty and Hierarchical Structure in Streams
  • J. Kleinberg
  • Computer Science
  • Data Mining and Knowledge Discovery
  • 2004
Latent Dirichlet Allocation
TextRank: Bringing Order into Text
TextRank: Bringing Order Into Texts
  • EMNLP
  • 2004