08/12/2020

Contextualized Word Embeddings Encode Aspects of Human-Like Word Sense Knowledge

Sathvik Nair, Mahesh Srinivasan, Stephan Meylan

Keywords:

Abstract Paper Similar Papers

Abstract: Understanding context-dependent variation in word meanings is a key aspect of human language comprehension supported by the lexicon. Lexicographic resources (e.g., WordNet) capture only some of this context-dependent variation; for example, they often do not encode how closely senses, or discretized word meanings, are related to one another. Our work investigates whether recent advances in NLP, specifically contextualized word embeddings, capture human-like distinctions between English word senses, such as polysemy and homonymy. We collect data from a behavioral, web-based experiment, in which participants provide judgments of the relatedness of multiple WordNet senses of a word in a two-dimensional spatial arrangement task. We find that participants’ judgments of the relatedness between senses are correlated with distances between senses in the BERT embedding space. Specifically, homonymous senses (e.g., bat as mammal vs. bat as sports equipment) are reliably more distant from one another in the embedding space than polysemous ones (e.g., chicken as animal vs. chicken as meat). Our findings point towards the potential utility of continuous-space representations of sense meanings.

The video of this talk cannot be embedded. You can watch it here:
https://underline.io/lecture/6479-contextualized-word-embeddings-encode-aspects-of-human-like-word-sense-knowledge
(Link will open in new window)
 0
 0
 0
 0
  Share    
This is an embedded video. Talk and the respective paper are published at COLING Workshops 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
 18:55 
 0
 0
 0
 0
 10:48 
 0
 0
 0
 0
 13:01 
 0
 0
 0
 0
 14:47 
 0
 0
 0
 0
 7:45 
 0
 0
 0
 0
 14:07 
 0
 0
 0
 0
 11:19 
 0
 0
 0
 0
 44:12 
 0
 0
 0
 0
 15:02 
 0
 0
 0
 0
 15:02 
 0
 0
 0
 0
 11:56 
 0
 0
 0
 0
 10:05 
 0
 0
 0
 0
 11:23 
 0
 0
 0
 0
 4:38 
 0
 0
 0
 0
 11:17 
 0
 0
 0
 0
 3:24 
 0
 0
 0
 0
 9:02 
 0
 0
 0
 0
 13:34 
 0
 0
 0
 0
 7:48 
 0
 0
 0
 0
 13:36 
 0
 0
 0
 0
 6:54 
 0
 0
 0
 0
 14:59 
 0
 0
 0
 0
 12:17 
 0
 0
 0
 0
 16:13 
 0
 0
 0
 0
 7:17 
 0
 0
 0
 0
 9:04 
 0
 0
 0
 0
 11:51 
 0
 0
 0
 0
 1:01 
 0
 0
 0
 0
 8:03 
 0
 0
 0
 0
 12:11 
 0
 0
 0
 0
 11:42 
 0
 0
 0
 0
 3:51 
 0
 0
 0
 0
 5:10 
 0
 0
 0
 0
 13:52 
 0
 0
 0
 0
 7:06 
 0
 0
 0
 0
 14:22 
 0
 0
 0
 0
 11:06 
 0
 0
 0
 0
 10:15 
 1
 0
 0
 0
 10:54 
 0
 0
 0
 0
 19:35