Rota

🚀 ROTA

Rapid Offense Text Autocoder, a machine learning model for converting offense text into offense codes

Criminal justice research often requires conversion of free-text offense descriptions into overall charge categories to aid analysis. ROTA addresses this need by efficiently converting offense text into offense codes, significantly reducing the manual effort for researchers.

ROTA Application hosted on Hugging Face Spaces: https://huggingface.co/spaces/rti-international/rota-app

🚀 Quick Start

Criminal justice research often requires conversion of free-text offense descriptions into overall charge categories to aid analysis. For example, the free-text offense of "eluding a police vehicle" would be coded to a charge category of "Obstruction - Law Enforcement". Since free-text offense descriptions aren't standardized and often need to be categorized in large volumes, this can result in a manual and time intensive process for researchers. ROTA is a machine learning model for converting offense text into offense codes.

Currently ROTA predicts the Charge Category of a given offense text. A charge category is one of the headings for offense codes in the 2009 NCRP Codebook: Appendix F.

The model was trained on publicly available data from a crosswalk containing offenses from all 50 states combined with three additional hand-labeled offense text datasets.

Charge Category Example

✨ Features

Data Preprocessing

The input text is standardized through a series of preprocessing steps. The text is first passed through a sequence of 500+ case-insensitive regular expressions that identify common misspellings and abbreviations and expand the text to a more full, correct English text. Some data-specific prefixes and suffixes are then removed from the text -- e.g. some states included a statute as a part of the text. Finally, punctuation (excluding dollar signs) are removed from the input, multiple spaces between words are removed, and the text is lowercased.

📚 Documentation

Cross-Validation Performance

This model was evaluated using 3-fold cross validation. Except where noted, numbers presented below are the mean value across the 3 folds.

The model in this repository is trained on all available data. Because of this, you can typically expect production performance to be (unknowably) better than the numbers presented below.

Overall Metrics

Note: These are the average of the values per fold, so macro avg is the average of the macro average of all categories per fold.

Per-Category Metrics

Note: support is the average number of observations predicted on per fold, so the total number of observations per class is roughly 3x support.

Using Confidence Scores

If we interpret the classification probability as a confidence score, we can use it to filter out predictions that the model isn't as confident about. We applied this process in 3-fold cross validation. The numbers presented below indicate how much of the prediction data is retained given a confidence score cutoff of p. We present the overall accuracy and MCC metrics as if the model was only evaluated on this subset of confident predictions.

📄 License

The license for this project is apache - 2.0.