02/02/2021

KEML: A Knowledge-Enriched Meta-Learning Framework for Lexical Relation Classification

Chengyu Wang, Minghui Qiu, Jun Huang, Xiaofeng He

Keywords:

Abstract Paper Similar Papers

Abstract: Lexical relations describe how concepts are semantically related, in the form of relation triples. The accurate prediction of lexical relations between concepts is challenging, due to the sparsity of patterns indicating the existence of such relations. We propose the Knowledge-Enriched Meta-Learning (KEML) framework to address lexical relation classification. In KEML, the LKB-BERT (Lexical Knowledge Base-BERT) model is first presented to learn concept representations from text corpora, with rich lexical knowledge injected by distant supervision. A probabilistic distribution of auxiliary tasks is defined to increase the model's ability to recognize different types of lexical relations. We further propose a neural classifier integrated with special relation recognition cells, in order to combine meta-learning over the auxiliary task distribution and supervised learning for LRC. Experiments over multiple datasets show KEML outperforms state-of-the-art methods.

The video of this talk cannot be embedded. You can watch it here:
https://slideslive.com/38948457
(Link will open in new window)
 0
 0
 0
 0
  Share    
This is an embedded video. Talk and the respective paper are published at AAAI 2021 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
 11:03 
 0
 0
 0
 0
 4:58 
 0
 0
 0
 0
 15:46 
 0
 0
 0
 0
 15:09 
 0
 0
 0
 0
 4:51 
 0
 0
 0
 0
 14:26 
 0
 0
 0
 0
 4:45 
 0
 0
 0
 0
 19:27 
 0
 0
 0
 0
 3:23 
 0
 0
 0
 0
 1:01 
 0
 0
 0
 0
 19:44 
 0
 0
 0
 0
 2:38 
 0
 0
 0
 0
 12:35 
 0
 0
 0
 0
 2:04 
 0
 0
 0
 0
 14:13 
 0
 0
 0
 0
 12:17 
 0
 0
 0
 0
 4:59 
 0
 0
 0
 0
 6:55 
 0
 0
 0
 0
 16:09 
 0
 0
 0
 0
 19:17 
 0
 0
 0
 0
 10:15 
 0
 0
 0
 0
 11:38 
 0
 0
 0
 0
 10:21 
 0
 0
 0
 0
 9:57 
 0
 0
 0
 0
 9:10 
 0
 0
 0
 0
 11:56 
 0
 0
 0
 0
 5:06 
 0
 0
 0
 0
 13:09 
 0
 0
 0
 0
 10:07 
 0
 0
 0
 0
 4:56 
 0
 0
 0
 0
 14:08 
 0
 0
 0
 0
 4:39 
 0
 0
 0
 0
 11:20 
 0
 0
 0
 0
 6:56 
 0
 0
 0
 0
 13:39 
 0
 0
 0
 0
 5:15 
 0
 0
 0
 0
 12:45 
 0
 0
 0
 0
 11:34 
 0
 0
 0
 0
 13:45 
 0
 0
 0
 0
 15:16