The Polish Divergent Association Task (DAT) Adaptation project is designed to bring the DAT, a measure of verbal creativity and divergent thinking, to the Polish language.

DAT involves generating 10 words that are as different from each other as possible. This project facilitates the administration and scoring of the task in Polish, providing a tool for researchers exploring creativity in participants' native language.

The DAT score is calculated based on the average distance between the given words. The distances are determined by analyzing how often words are used together in similar contexts, utilizing pre-trained GloVe model.

Words with closer associations have smaller distances. Creative ability is associated with thinking of words that are more unrelated to each other, resulting in a higher DAT score.

Read the authors' manuscript to learn more about the task.

1. Clone the repository:

   git clone https://github.com/ag-bart/datpl.git
   cd datpl

2. Create a virtual environment (optional but recommended):

   python -m venv venv
   source venv/bin/activate  

3. Install the required packages:

   pip install -r requirements.txt

4. Run the setup script:

   sh setup.sh

   This script performs the following tasks:

   • Downloads the GloVe model and unpacks it.
   • Filters words and vectors from the GloVe model, retaining only those found in the included dictionary file words.txt.
   • Creates an SQLite database using the filtered words and vectors.
   • Performs tests to ensure the database is correctly configured.
   • Removes temporary files, leaving only the database if tests pass.

In the config.ini file, you can specify the path to the data file for computation. Locate the [Data] section and update the data_file_path setting accordingly.

[Data]
 data_file_path = data/dat-data.xlsx

1. Launch Jupyter Notebook:

   jupyter notebook

2. Open the example_notebook.ipynb file in the Jupyter interface.

3. Run the notebook cells one by one or use the "Run All" option.

Global Vectors for Word Representation by: Dadas, S. (2019). Polish NLP resources (Version 1.0) [Computer software]. https://github.com/sdadas/polish-nlp-resources

Olson, J. A., Nahas, J., Chmoulevitch, D., Cropper, S. J., & Webb, M. E. (2021). Naming unrelated words predicts creativity. Proceedings of the National Academy of Sciences of the United States of America, 118(25). https://doi.org/10.1073/pnas.2022340118