The Future of Legal Content Interpreting: How NLP Can Help?

Legal content is dense, complicated, and often less than straightforward. As such, it’s crucial to understand what you’re reading and make decisions based on that information. That’s where natural language processing (NLP) comes in.

What is NLP?

NLP, or natural language processing, is a field of computer science and artificial intelligence concerned with the interactions between computers and human (natural) languages, mainly how to program computers to process and analyze large amounts of natural language data.

NLP interprets legal content through algorithms that identify relevant information from unstructured text. This process can extract key phrases, concepts, and entities from a document for further analysis. Additionally, NLP can generate summaries of legal documents or identify critical issues within a document.

Why Use NLP for Interpreting Legal Content?

There are many reasons to use natural language processing (NLP) when interpreting legal content. First, NLP can help you to identify the essential information in a document. This is especially helpful when dealing with long and complex legal documents. Second, NLP can help you understand the text’s meaning by extracting key concepts and ideas. This is extremely helpful in understanding the implications of a legal document. Finally, NLP can help you to identify relationships between different concepts in the text. This can help identify issues that may not be immediately apparent.
Overall, NLP can be a very helpful tool when interpreting legal content. It can help you to identify the most crucial information, understand the meaning of the text, and identify relationships between different concepts.

Applications of NLP in the Legal Field

There are many potential applications for NLP in the legal field. Here are a few examples:

  1. Automated contract analysis: NLP can automatically analyze contracts and identify critical provisions, such as parties, obligations, and termination clauses. This can save time and improve accuracy compared to manual contract review.
  2. Legal research: NLP can quickly search large volumes of legal documents (e.g., court opinions) for relevant information. This can save time and improve accuracy compared to traditional keyword search methods.
  3. Sentiment analysis of legal documents: NLP can be used to analyze the sentiment of legal documents, such as court opinions, to identify positive or negative feelings towards specific individuals or entities. This information could be helpful for lawyers when making strategic decisions about cases.
  4. Predictive analytics for litigation: NLP can predict the outcome of litigation based on past cases with similar facts and circumstances. This information could be helpful for lawyers when deciding whether to settle a claim or take it to trial.
  5. Automated document summarization: NLP can automatically summarize legal documents, such as court opinions, to save time and improve accuracy. This information could be helpful for lawyers who need a quick case overview.
  6. Entity extraction from legal documents: NLP can automatically extract entities, such as names of people and organizations, from legal documents. This information could be helpful for lawyers when they need to find information about specific individuals or entities quickly.

Let’s explore some real examples extracted from John Snow LABS.

What is John Snow LABS company?

John Snow Labs, an AI and NLP for a healthcare, legal, and finance company, provides state-of-the-art software, models, and data to help healthcare, legal, and life science organizations build, deploy, and operate AI projects. Click here to go to their LinkedIn page.

They have a model called “Spark NLP for Legal” to work on Legal documents. Let’s deeply in.

Introducing Spark NLP for Legal

What’s in the Spark NLP for Legal?

State-of-the-art software + pre-trained legal-specific models

One of the most common uses of NLP is Entity Recognition. Let’s try using a Portuguese document.

If you want yourself try this example, click here. Also, you can look at the Python code on Google Colab here.

Another exciting use of NLP technology is to extract relations between parties in an agreement. Look at the example below.

This model returns something like this to organize and save the insights.

Identified relations
Identified chunks

If you want yourself try this example, click here.

You can save this information with each document and use it to analyze and predict insights. This tool is so powerful and is available to work in multiple languages. In addition, you can look at other attractive models in John Snow LABS in healthcare and finance.


NLP is a powerful tool that can be used for various tasks, including the interpretation of legal content. In this article, we’ve looked at how NLP can be used to interpret legal documents and how it can be used to improve the accuracy of translations. We hope this has given you a better understanding of how NLP can be used in the legal industry and how it can benefit your business.
If you found this article helpful, please share it with your network! And if you have any questions or comments, please feel free to leave them below.

Let’s share more articles talking about Spark NLP For Legal:

Spark NLP For Legal 1.0.0: Over 300+ new state-of-the-art models in multiple languages!

Legal NLP 1.1.0 for Spark NLP has been released

Legal NLP 1.2.0 for Spark NLP has been released!

That’s it for today!

Author: Lawrence Teixeira

Lawrence is a senior technology delivery lead with over 17 years of experience as a CTO and CIO in intellectual property companies. He has experience in both Agile and Waterfall development methodologies. He has a solid technical background in IT and excellent management skills with over 25 years in the area, delivering advanced systems projects and data analytics. Lawrence has hands-on experience building and deploying intellectual property systems, business intelligence, data warehousing, and building bots for RPA and data collection. He also knows PMP, Agile, Scrum, DevOps, ITIL, CMMI, and ISO/IEC 27001.

Leave a Reply

%d bloggers like this: