NLP of Legal Texts?

Question

I have a corpus of a few 100-thousand legal documents (mostly from the European Union) – laws, commentary, court documents etc. I am trying to algorithmically make some sense of them.

I have modeled the known relationships (temporal, this-changes-that, etc). But on the single-document level, I wish I had better tools to allow fast comprehension. I am open for ideas, but here's a more specific question:

For example: are there NLP methods to determine the relevant/controversial parts of documents as opposed to boilerplate? The recently leaked TTIP papers are thousands of pages with data tables, but one sentence somewhere in there may destroy an industry.

I played around with google's new Parsey McParface, and other NLP solutions in the past, but while they work impressively well, I am not sure how good they are at isolating meaning.

Answer 1

In order to make sense out of documents you need to perform some sort of semantic analysis. You have two main possibilities with their exemples:

Use Frame Semantics: http://www.cs.cmu.edu/~ark/SEMAFOR/

Use Semantic Role Labeling (SRL): http://cogcomp.org/page/demo_view/srl

Once you are able to extract information from the documents then you may apply some post-processing to determine which information is relevant. Finding which information is relevant is task related and I don't think you can find a generic tool that extracts "the relevant" information.