FASTLAW

Fastlaw is a law-specific word embedding model trained on a large corpus of law-related text data provided by the Caselaw Access Project (CAP). Fastlaw was computed using Spark, a distributed cluster-computing framework, and FastText, a library for efficient word representation learning.

The repository is composed of three main bash scripts.

• download_bulk_data.sh allows to download all jurisdiction data from the CAP website. The script accomplishes several tasks such as downloading a single file or a list of jurisdiction files, downloading all data, or downloading only public jurisdiction.

• wordembedding.sh permits to construct the word embedding. The most important parameter is the slug (-s) that represents the jurisdiction data based on which the word embedding will be computed.

• evaluation.sh allows to assess different models of Fastlaw against FastText. The evaluation is performed on a text classification task where the objective is to distinguish civil vs. criminal cases.

Technical report and results

Here you can download the technical report. The aim of this document is to provide additional details about the implementation and the respective challenges encountered during development.

Installation

The pre-processing steps that transform the initial jsonl file into the single tokenized txt file make use of Apache PySpark and Apache Hadoop. A running version of this software (preferably the latest one), is needed.

Python dependencies Python 3 is required to execute the code. Moreover, extra dependencies needed can be downloaded with pip:

pip install -r requirements.txt

Download of bulk data

As now, CAP has released to the public three jurisdictions:

• Arkansas, 136.7 MB (txt)
• Illinois, 463.0 MB (txt)
• New Mexico, 63.9 MB (txt)

To being able to download the private jurisdiction, an account on the CAP website with unmetered access is needed. Once accepted, CAP will provide an API key token used to download all the jurisdiction data.

download_bulk_data.sh is used to download the data. It accepts the following arguments:

• -s (slug) download only the jurisdiction of the passed slug or list of slug
• -a (all) download all data

In case no parameters are given, the script download only bulk public data.

Token. In order to download private jurisdiction, the following line: token=YOUR_TOKEN has to be added in the config file in the download_bulk_data folder

Wordembedding

wordembedding.sh arguments:

• -s (slug) it can be a single slug or all. Default is am-samoa. If all is passed, the word embedding will be computed using all jsonl file present in the data/bulk folder.
• -c (cap) if passed, do not remove non-existing English words. When training a large dataset, it's advised to provide this argument for faster results.
• -d (dim) dimension of the word embedding. Default: 300.

Example: ./wordembedding.sh -s us -c

Evaluation

evaluation.sh arguments:

• -s (slug) it can be a single slug or a list of slugs. The default value is a list of small jurisdiction. If multiple value are passed, each jurisdiction will be computed separately.
• -n (number of training cases) the number of training cases selected from a single jurisdiction. It can be a single or a list of values.

Example: ./evaluation.sh -s "am-samoa tex" -n "100 500 100"