WSBM_WUG_Modelling

Source Code and Experiments for

• Dominik Schlechtweg, Enrique Castaneda, Jonas Kuhn, and Sabine Schulte im Walde. 2021. Modeling Sense Structure in Word Usage Graphs with the Weighted Stochastic Block Model. In Proceedings of the 10th Joint Conference on Lexical and Computational Semantics.

If you use this repository for academic research, please cite us. Find data and further code on the WUGsite.

Description

In this project we present 2 function files:

• wsbm.py contains util functions to handle wsbms, for instance, create a graph-tool graph out of a wug sem_eval_2_graph_tool, fit a graph-tool graph given a distribution minimize or find the best distribution for a given graph find_best_distribution by selecting the fitted model with distribution that yields the smallest entropy.

• plot_utils.py contains util functions to handle everything wrt. plots, for instance plots of graph-tool graphs draw, plots of histograms showing the relation of a graph distribution between and inside blocks plot_values_oneside and plot_values, as well as a function to plot the histogram showing amount of distributions that best fit the graphs plot_dist_dist and a function to combine the plots of plot_values_oneside into a detailed image showing all the distribution combinations combine_community_plots.

We also present the scripts used to fit the WUGs and generate the different plots:

• save_semeval_wsbm_info.py computes the best distribution describing the graphs and pickles two dictionaries: one containing the name of this distribution per graph and the graph partition based on this distribution graph-tool state, and another containing the accuracy of this partition.

• draw_wugs.py saves the fitted WUG plots.

• plot_accuracies.py and plot_number_of_blocks.py generate the plots for the accuracy and the number of blocks of the best fit (using the values saved in save_semeval_wsbm_info.py)

• plot_distributions.py generates the plots with the distribution relations between and inside blocks as well as the prediction that the model would make based on the best fit distribution parameters. WARNING this script takes a long time to run, since it infers the parameters of all posible combinations (inside blocks and between blocks) for all graphs

• plot_stats_best.py generates the plot with the amount of distributions best fitting the graphs

Usage

Note that the script parameters vary, but are always paths to save the output of the script or to get the saved data as input.

Example of use:

python draw_wugs.py <path_to_graphs> <path_to_pickled_data> <output_path>

For a better understanding of the code and to make it easier to see it working hands on, we put our experiment scripts in a notebook in the example directory than can be executed directly in colab. Notice that the experiments processing all the graphs in a directory are slightly modified in the notebook to only fit and plot a single graph, making it less time consuming.

BibTex

@inproceedings{Schlechtweg2021wsbm,
	Author = {Schlechtweg, Dominik and Castaneda, Enrique and Kuhn, Jonas and {Schulte im Walde}, Sabine},
	Booktitle = {{Proceedings of the 10th Joint Conference on Lexical and Computational Semantics}},
	Title = {Modeling Sense Structure in Word Usage Graphs with the Weighted Stochastic Block Model},
	Year = {2021}
}