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First forays into natural language processing — get rid of a line or keep it?

First forays into natural language processing — get rid of a line or keep it?
This post is part of a series on my Data Science journey with PDPC Decisions. Check it out for more posts on visualisations, natural languge processing, data extraction and processing!

Avid followers of Love Law Robots will know that I have been hard at creating a corpus of Personal Data Protection Commission decisions. Downloading them and pre-processing them has taken a lot of work! However, it has managed to help me create interesting charts that shows insight at a macro level. How many decisions are released in a year and how long have they been? What decisions refer to each other in a network?

Unfortunately, what I would really to do is natural language processing. A robot should analyse text and make conclusions from it. This is much closer to the bread and butter of what lawyers do. I have been poking around spaCy, but using their regular expression function doesn’t really cut it.

This is not going to be the post where I say I trained a model to ask what the ratio decendi of a decision is. Part of the difficulty is finding a problem that is solvable given my current learning. So I have picked something that is useful and can be implemented fast.

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