Word Vector Evaluation

Step 1: Choose Pre-trained Vector


Select? Name Dimensions Vocabulary Reference
Metaoptimize 50 268810 Turian et al, 2010
Senna 50 130000 Collobert et al, 2011
RNN 80 82390 Mikolov et al, 2011
RNN 640 82390 Mikolov et al, 2011
Global Context 50 100232 Huang et al, 2012
Skip-Gram 80 180834 Mikolov et al 2013
Multilingual 512 180834 Faruqui and Dyer, 2014

Step 2: Plot Your Words




Word Pair Similarity Ranking


No. Task Name Word pairs Reference Pairs found Correlation
1 WS-353 353 Finkelstein et. al, 2002
2 WS-353-SIM 203 Agirre et. al, 2009
3 WS-353-REL 252 Agirre et. al, 2009
4 MC-30 30 Miller and Charles, 1930
5 RG-65 65 R and G, 1965
6 Rare-Word 2034 Luong et. al, 2013
7 MEN 3000 Bruni et. al, 2012
8 MTurk-287 287 Radinsky et. al, 2011
9 MTurk-771 771 Halawi and Dror, 2012
10 YP-130 130 Yang and Powers, 2006
11 SimLex-999 999 Hill et. al, 2014
12 Verb-144 144 Baker et. al, 2014
13 SimVerb-3500 3500 Gerz et al., 2016

Default Word Plots

t-SNE tool, Maaten and Hinton 2008

Antonym and Synonyms


Male and Female

Reference


If you use this website, please cite the following paper:
@InProceedings{faruqui-2014:SystemDemo,
  author    = {Faruqui, Manaal  and  Dyer, Chris},
  title     = {Community Evaluation and Exchange of Word Vectors at wordvectors.org},
  booktitle = {Proceedings of the 52nd Annual Meeting of the 
               Association for Computational Linguistics: System Demonstrations},
  month     = {June},
  year      = {2014},
  address   = {Baltimore, USA},
  publisher = {Association for Computational Linguistics}
}