25/07/2020

High-precision extraction of emerging concepts from scientific literature

Daniel King, Doug Downey, Daniel S. Weld

Keywords: concept extraction, citation graph, scientific literature

Abstract Paper Similar Papers

Abstract: 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 relies on a simple but novel intuition: each scientific concept is likely to be introduced or popularized by a single paper that is disproportionately cited by subsequent papers mentioning the concept. From a corpus of computer science papers on arXiv, we find that our method achieves a Precision@1000 of 99

The video of this talk cannot be embedded. You can watch it here:
https://dl.acm.org/doi/10.1145/3397271.3401235#sec-supp
(Link will open in new window)
 0
 0
 0
 0
  Share    
This is an embedded video. Talk and the respective paper are published at SIGIR 2020 virtual conference. If you are one of the authors of the paper and want to manage your upload, see the question "My papertalk has been externally embedded..." in the FAQ section.

Comments

Post Comment
no comments yet
code of conduct: tbd Characters remaining: 140

Similar Papers

 0
 0
 0
 0
 13:40 
 0
 0
 0
 0
 14:48 
 0
 0
 0
 0
 14:20 
 0
 0
 0
 0
 5:27 
 0
 0
 0
 0
 19:10 
 0
 0
 0
 0
 15:09 
 0
 0
 0
 0
 24:57 
 0
 0
 0
 0
 9:10 
 0
 0
 0
 0
 15:30 
 0
 0
 0
 0
 11:17 
 0
 0
 0
 0
 19:41 
 0
 0
 0
 0
 3:02 
 0
 0
 0
 0
 3:28 
 0
 0
 0
 0
 13:54 
 0
 0
 0
 0
 6:17 
 0
 0
 0
 0
 14:48 
 0
 0
 0
 0
 4:59 
 0
 0
 0
 0
 15:02 
 0
 0
 0
 0
 6:36 
 0
 0
 0
 0
 14:50 
 0
 0
 0
 0
 11:27 
 0
 0
 0
 0
 5:00 
 0
 0
 1
 1
 14:35 
 0
 0
 0
 0
 3:16 
 0
 0
 0
 0
 11:38 
 0
 0
 0
 0
 5:38 
 0
 0
 0
 0
 15:31 
 0
 0
 0
 0
 17:58 
 0
 0
 0
 0
 22:53 
 0
 0
 0
 0
 17:29 
 0
 0
 0
 0
 16:44 
 0
 0
 0
 0
 1:01 
 0
 0
 0
 0
 14:41 
 0
 0
 0
 0
 13:55 
 0
 0
 0
 0
 13:57 
 0
 0
 0
 0
 17:35 
 0
 0
 0
 0
 9:41 
 0
 0
 0
 0
 5:04 
 0
 0
 0
 0
 6:21 
 0
 0
 0
 0
 22:04