Is NLP suitable for my legal contract parsing problem? My company has a product that involves the extraction of a variety of fields from legal contract PDFs. The current approach is very time consuming and messy, and I am exploring if NLP is a suitable alternative.
The PDFs that need to be parsed usually follow one of a number of &quot;templates&quot;. Within a template, almost all of the documents are the same, except for 20 or so specific fields we are trying to extract. That being said, there are sometimes slight inconsistencies such as missing/added spaces, punctation, etc. Currently there are about 15 templates, but sometimes another gets added. Most of the data that needs to be extracted is in tabular form, but a few values are littered in paragraph text.
With the current, non-ML approach, we use a proprietary PDF parser that extracts out all of the tables into a json format. This parser must be custom configured for every new PDF template that appears, and this is time consuming. After converting to json, a custom regex expression must be made for each value in each template, leading to several hundred regexes total. Usually this approach is pretty accurate, but sometimes the slight inconsistencies can break it, meaning a the regex needs to be revised.
I'm wondering if NLP Named Entity Recognition would be a cleaner solution to this problem. My idea is to label all of the values in a few hundred sample documents and then train a custom NER model in a library like Spacy or Flair. Ideally, we could feed in the raw, extracted text from the PDFs instead of having to configure the custom parser to extract json.
The advantage I see in using the NLP approach is that we wouldn't have to configure the custom parser and write a bunch of regexes every time a new template appears. At worst, we would have to label a few of the new documents every time a template is added, which would presumably be faster and easier than the current approach. I think we could also generate tons of synthetic training data easily by swapping labeled values between different documents.
I am concerned that, using an ML approach, we wouldn't be able to achieve near perfect accuracy, which is a requirement. I'm also not sure how well NLP can perform on raw text from tables as opposed to paragraphs. The nice thing is that the documents tend to be very similar within templates.
I've never done NLP before, so I'm wondering if anyone here thinks this approach would be worthwhile exploring. If this is feasible, does anyone have suggestions on how to get the best results?
Here's what I would have done.

extract raw data from the the pdfs.
use rule based NER from spacy(it's quite fast), but it will require some manual pattern making.
Also apply multiprocessing if you can.
https://spacy.io/usage/rule-based-matching
You can also write a document classification to segregate the templates and send the templates to appropriate code,running parallely.
Also checkout blackstone library for legal texts, it might be of some use https://spacy.io/universe/project/blackstone