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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 (email@example.com). 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. DATA and CODE: We include the original (unfiltered) data from the crowd-sourcing experiment. data/AdjMainR.csv 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 code/nn_eval_and_plot.py 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 (firstname.lastname@example.org) for more information in the meantime.