Main content

Home

Menu

Loading wiki pages...

View
Wiki Version:
Please see the Supplementary Information (available at https://doi.org/10.1038/s41598-023-42327-3) for information regarding the content of this repository. - Details regarding the used corpora are given in section S3 of the Supplementary Information (including details where to download/how to obtain the raw text files). - Details regarding code and data are given in section S4 of the Supplementary Information. - Data preparation, management and analysis were done in Stata 14.2 and in Stata 18. Commented code is available in the folder 'Stata Code'. - A replication of the key results was done in R, the corresponding scripts are available in the folder 'R Code'. - Supplementary Table 4 lists and describes the variables included in the final dataset that is available as a csv file ("textcomp_data.csv") in the folder: Data. - In the folder 'PPM in action', you will find details regarding our illustration of PPM as a text predictor including training data, trained (unsmoothed and smoothed) n-gram models and an open source Java program to interactively test PPM. In addition, you can use the 'smoothLM.ipynb' jupyter notebook to generate examples for any text file. To demonstrate that PPM learns to predict with growing input, examples are produced (both on the level of characters and on the level of words) based on 5%, 10%, 20%, 40%, 80% and 100% of the input text. In case you have any questions regarding all **code**, **data** or **analyses**, please do not hesitate to contact us: - koplenig at ids-mannheim.de - wolfer at ids-mannheim.de - meyer at ids-mannheim.de **We really mean it!**
OSF does not support the use of Internet Explorer. For optimal performance, please switch to another browser.
Accept
This website relies on cookies to help provide a better user experience. By clicking Accept or continuing to use the site, you agree. For more information, see our Privacy Policy and information on cookie use.
Accept
×

Start managing your projects on the OSF today.

Free and easy to use, the Open Science Framework supports the entire research lifecycle: planning, execution, reporting, archiving, and discovery.