Is Natural Language Processing Advanced Enough to Tackle Legal Documentation? Is Natural Language Processing Advanced Enough to Tackle Legal Documentation?
Natural language processing (NLP) is one of the most practical AI fields today. This technology is the driving force behind chatbots,... Is Natural Language Processing Advanced Enough to Tackle Legal Documentation?

Natural language processing (NLP) is one of the most practical AI fields today. This technology is the driving force behind chatbots, smart speakers, and spell-checkers, and it could go further. Many law firms have started to take note of NLP’s potential.

The legal industry seems like a textbook use case for tools like NLP. It involves extensive hours of data-heavy, repetitive tasks with a slim margin for error. However, its complexity and the severe implications of mistakes in the field make it an intimidating prospect.

Is NLP advanced enough to tackle complex legal documents?


Current Use Cases for NLP in Legal Documentation

NLP has already broken into the legal sector, regardless of whether it’s truly sufficient for the job yet. The most common use for NLP in law is document review and management, where AI algorithms analyze and highlight relevant information.

Legal AI companies like Levartis offer ready-to-use models that can find relevant or potentially problematic clauses in millions of documents. While this speed is impressive, these tools often aren’t sophisticated enough to work without context. Human workers still have to upload documents and training datasets for it to learn what’s relevant.

Other legal NLP models review contracts to find clauses of concern. Some can analyze roughly 500 common provisions and contract types in multiple languages. This analysis helps find potential oversights, loopholes, or fine print that an attorney’s client should be aware of.

A less complex application of NLP in legal work is chatbot support. They can’t provide legal advice, but they can learn and categorize clients’ needs before passing them to a human representative.

Advantages of NLP in Law

It’s easy to understand how NLP has grown so quickly in legal documentation. Processes like contract review and information discovery are long, tiresome tasks that are best to automate if possible.

For example, probating a will can take several years with traditional, manual processes. A lot of time is spent looking through documents to discover relevant information, something an NLP model could do in mere minutes.

AI is generally better than humans at data-heavy tasks, so it may also reduce errors. While a lawyer or paralegal may get tired or distracted after reading 100 pages of court proceedings, an NLP model won’t.

Remaining Obstacles

Some substantial challenges remain despite NLP’s potential and growing uses in law. Many of today’s models are too limited or simple for extensive use in legal documentation. The average parole hearing transcript is 10,000 words long, while most current models only have experience with 500-1,000-word documents.

Named entity recognition (NRE), which many NLP models rely on, may also be insufficient for legal work. Lengthy legal documents, especially court proceedings, may not always refer to an entity by the same name, making it harder for these models to highlight relevant information.

Multistep questions also remain a challenge for today’s NLP models, yet these are common in law. Similarly, many legal issues are too nuanced for black-and-white, “if this, then that” reasoning. The definition of a legal error changes depending on the application of abstract concepts, which AI has a hard time with.

There are also ethical implications to consider. Digitizing data to put it through an NLP algorithm could expose it to cybercrime, endangering clients’ privacy. Law firms and their customers that aren’t aware of this risk may unintentionally make sensitive data vulnerable.

Similarly, legal offices may not understand NLP’s limitations. Relying entirely on these imperfect tools may result in critical oversights or mistakes. In the legal field, those mistakes could result in fines or imprisonment.

Should Law Offices Use NLP In Its Current State?

Considering these benefits and disadvantages, is NLP ready for legal documentation? Yes and no.

Simpler, less nuanced applications like chatbots and file organizing are ideal use cases for NLP in law today. Law firms can also comfortably use these models for initial document review and contract drafting, as long as humans have the final say.

Research shows that even deep learning models struggle to classify legal texts correctly, and these tools’ ethical consequences are substantial. Those roadblocks are hard to ignore, given the high stakes in the legal industry. NLP can handle simpler legal work, but it’s insufficient for in-depth research and law interpretation.

Law professionals who use these tools must be aware of their shortcomings to avoid overapplying with them. Similarly, developers should tell end-users that NLP cannot be the final step in a legal process due to its limitations.

AI in Law Is Promising, but Challenges Remain

NLP models may one day be sophisticated enough to handle more nuanced legal documentation. For now, though, it’s best suited for administrative work. AI has come a long way, and its potential is impressive, but the legal sector is too abstract and the consequences are too severe for widespread adoption.

Editor’s Note:

Learn More About NLP Frameworks and Skills at ODSC East 2022

At ODSC East 2022, we have an entire track devoted to NLP. Learn NLP skills and platforms like the ones listed above. Here are a few sessions scheduled so far:

  • Intro to NLP: Text Categorization and Topic Modeling: Sanghamitra Deb, PhD | Staff | Data Scientist | Chegg
  • Spark NLP for Healthcare: Modular Approach to Solve Problems at Scale in Healthcare NLP: Veysel Kocaman, PhD | Lead Data Scientist | John Snow Labs
  • 🤗 Transformers & 🤗 Datasets for Research and Production: Patrick von Platen | Research Engineer | Hugging Face
  • Natural Language Processing in Accelerating Business Growth: Sameer Maskey, PhD | Founder & CEO | Fusemachines
  • Evolution of NLP and its Underpinnings:  Chengyin Eng | Senior Data Scientist | Databricks
April Miller

April Miller

April Miller is a staff writer at ReHack Magazine who specializes in AI, machine learning while writing on topics across the technology sphere. You can find her work on ReHack.com and by following ReHack's Twitter page.