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This is the resouce described in the LREC 2018 paper: 
"Grounding Gradable Adjectives through Crowdsourcing"

If you have any questions or issues, please contact Becky Sharp

The resource consists of four files, each corresponding to one version of the 
gradable adjective groundings:

 -- the full set of adjectives (98) with both mean (mu) and stdev (sigma)
 -- the full set of adjectives with only mean
 -- the high-frequency subset (30)  with both mean and stdev
 -- the high-frequency subset with only mean

The first line of each file is the header that describes the data organization.

Usage example:
 - if you have an known item (e.g., mean rainfall = 40 in/yr, stdev = 3in) and 
you want to know the impact of a gradable adjective (i.e., a *small* increase):
	1) choose a model 
		(ex: full model)
	2) find the row corresponding to the desired adjective
		(ex: small	1.034e-05	-0.001123	-1.7094)
	3) This row gives the linear model, so plug in your known mean 
	   (and stdev, if applicable):
		(ex: logDeviations = -1.7094 + (1.034e-05*40) + (-0.001123*3))
	4) Convert to the true predicted change:
		(ex: predChange = (e^(logDeviations) * stdev))
		(here, because it's an increase, this would then be added to 
		 the mean).

A python demo script is included.  To use:
> python demo_gradable.py
And follow the prompts.

We include the original (unfiltered) data from the crowd-sourcing experiment.
We additionally include the R code to reproduce all analyses and plots, though
please note that the code is not an end-to-end script, but rather a set of commands.
Also, paths to data will likely need to be modified depending on where/how you
store the released data.

Predictions of the NN model on both seen and unseen adjectives are included in data/.
The code for evaluating these models as well as for generating the input used
to make the MSE vs. Variance plot for unseen adjectives is included in 

We will soon release the code for the neural network model as well as all prompts
given to participants in the crowd-sourcing task.  Please contact 
Becky Sharp (bsharp@email.arizona.edu) for more information in the meantime.