Islamic law and Natural Language Processing: An Experiment with Semantic Similarity
Channel: Mairaj Syed
Date: 2020-06-02

This video walks through a Python Jupyter notebook that attempts to discover the presence of a legal canon in a premodern work of Islamic law by applying natural language processing (NLP) techniques.

The notebook can be found here: https://github.com/mairaj-syed/canons-in-Islamic-law-ethics 

It shows how one may obtain an electronic copy of a premodern Islamic legal text, partition it, clean it, and segment it in order to obtain semantic similarity scores from Google AI workshop's Semantic Similarity for Natural Language cloud service.

For an analysis of why canons (called qawāʿid in Arabic) are important in Islamic law, see: https://islamiclaw.blog/2020/05/21/canons-qawa%ca%bfid-and-reasoning-in-islamic-law-and-ethics/.

For an overview of the motivation behind the semantic similarity and Islamic law experiment, along with the pre-processing steps necessary to get semantic similarity scores, see: https://islamiclaw.blog/2020/06/02/an-experiment-in-natural-language-processing-machine-learning-and-islamic-law-part-1/

For a presentation and interpretation of the results of the experiment, along with thoughts on the future direction of Islamic law and ML/NLP, see:
https://islamiclaw.blog/2020/06/02/an-experiment-in-natural-language-processing-machine-learning-and-islamic-law-part-2/

Here are some important urls mentioned in the video:

To download Jupyter notebook, see: https://www.anaconda.com/products/individual

To apply for Google's semantic similarity cloud service, see: 
https://events.withgoogle.com/ai-workshop/

For OpenITI's repository of Islamic texts, see: https://github.com/OpenITI

For OpenITI's searchable catalog of texts, see:  https://kitab-corpus-metadata.azurewebsites.net/