19/10/2020

A large test collection for entity aspect linking

Jordan Ramsdell, Laura Dietz

Keywords: dataset, reference method, entity aspect linking

Abstract Paper Similar Papers

Abstract: Given a text with entity links, the task of entity aspect linking is to identify which aspect of an entity is referred to in the context. For example, if a text passage mentions the entity "USA”, is USA mentioned in the context of the 2008 financial crisis, American cuisine, or else? Complementing efforts of Nanni et al (2018), we provide a large-scale test collection which is derived from Wikipedia hyperlinks in a dump from 01/01/2020. Furthermore, we offer strong baselines with results and broken-out feature sets to stimulate more research in this area. Data, code, feature sets, runfiles and results are released under a CC-SA license and offered on our aspect linking resource web page http://www.cs.unh.edu/ dietz/eal-dataset-2020/

The video of this talk cannot be embedded. You can watch it here:
https://dl.acm.org/doi/10.1145/3340531.3412875#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 CIKM 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
 15:11 
 0
 0
 0
 0
 11:53 
 0
 0
 0
 0
 11:15 
 0
 0
 0
 0
 5:00 
 0
 0
 0
 0
 6:12 
 0
 0
 0
 0
 11:23 
 0
 0
 0
 0
 5:43 
 0
 0
 0
 0
 10:14 
 0
 0
 0
 0
 7:14 
 0
 0
 0
 0
 10:15 
 0
 0
 0
 0
 12:01 
 0
 0
 0
 0
 16:11 
 0
 0
 0
 0
 12:05 
 0
 0
 0
 0
 29:33 
 0
 0
 0
 0
 11:38 
 0
 0
 0
 0
 10:01 
 0
 0
 0
 0
 11:22 
 0
 0
 0
 0
 14:14 
 0
 0
 0
 0
 12:01 
 0
 0
 0
 1
 2:44 
 0
 0
 0
 0
 13:59 
 0
 0
 0
 0
 7:18 
 0
 0
 0
 0
 7:03 
 0
 0
 0
 0
 6:32 
 0
 0
 0
 0
 9:43 
 0
 0
 0
 0
 7:04 
 0
 0
 0
 0
 8:53 
 0
 0
 0
 0
 4:44 
 0
 0
 0
 0
 15:43 
 0
 0
 0
 0
 11:44 
 0
 0
 0
 0
 11:53 
 0
 0
 0
 0
 18:14 
 0
 0
 0
 0
 4:48 
 0
 0
 0
 0
 11:36 
 0
 0
 0
 0
 12:58 
 0
 0
 0
 0
 7:52 
 0
 0
 0
 0
 14:12 
 0
 0
 0
 0
 19:30 
 0
 0
 0
 0
 4:42 
 0
 0
 0
 0
 12:04