NLP in the Legal Industry

The legal industry may be one of the oldest and most established businesses in the world, tracing its origins to ancient Greece, but it is also one of the most complex, relying heavily on in-domain knowledge and manual processes. Once slow and sluggish, natural language processing (NLP) has kicked off a legal industry evolution.

In just a few short years, NLP has proved its worth to early adopters – from speeding research to reviewing contracts – and more and more leading legal departments are tapping into this game-changing technology.

According to a recent Markets and Markets report, the NLP market size is already worth around $16 billion and will reach approximately $50 billion by 2027. Today, if you’re not exploring NLP, you’re at very real risk of losing pace to the competition. Meanwhile, PWC has found that the global economic growth that AI will drive by 2030 is estimated at $15.7 trillion. Those who will benefit from this colossal growth will be the organizations that find practical ways to apply this technology to their business challenges.

Challenges

The law is a deeply complex field, defined by highly accurate, repetitive, and often manual processes, which can only be executed by those with domain expertise. For example, claims processes can encompass multiple claimants – often well into the thousands – and must follow strict processes to determine the outcome of claims against a legal framework. Even something as simple as giving legal advice to a client can come with a heavy workload and high risks. Legal research and information discovery can also span years for a single case, especially when teams are required to sift manually through documents.

NLP can vastly accelerate these processes – even adding an additional layer of accuracy which can be hard for humans to achieve. In fact, according to How Does NLP Benefit the Legal System: A Summary of Legal Artificial Intelligence: “Retrieving and understanding legal documents takes lots of time, even for legal professionals. Therefore, a qualified system of LegalAI should reduce the time consumption of these tedious jobs and benefit the legal system.”

Solutions

NLP is already used to address a number of business challenges which are highly applicable to the legal industry, including:

Search and Discovery

• Content mining and smart document understanding – NLP can significantly speed the legal search and discovery process by mining databases for high-quality documents, and suggesting semantically similar documents and texts. Austrian legal publishing house Manz, whose online legal database, RDB Rechtsdatenbank, houses more than three million documents, leverages deepset Cloud to enable customers to research case law, review documents, and find 30 facets to their legal problem with just a single query – making for a faster, more accurate, and more enjoyable research process.
• Text extraction and classification – NLP helps legal professionals with cross-referencing, research, and classification by analyzing relationships and patterns in unstructured text. Further, named entity recognition (NER) identifies and categorizes key information within texts for a smoother search and classification process. Manz uses NLP to process new documents with an underlying language model to seamlessly incorporate them into its document similarity search feature.

Client Relations

• Question answering – NLP can process questions structured in human language as either text or voice data – even if the person asking the question does not fully understand legal terminology. Performing lemmatization, part of speech tagging, and disambiguation, NLP is able to analyze the question’s sentiment and intent, thus connecting questions to the correct answer – taking the burden off legal professionals to manually respond to simple queries. In fact, London-based Allen & Overy is experimenting with law-focused generative AI to answer simple questions about the law, draft documents, and draft messages to clients.

With the value of NLP already well proven, there are also even more advanced applications of this technology on the horizon. The research paper Predicting Judicial Decisions of the European Court of Human Rights: A Natural Language Processing Perspective, explored the first systematic study on predicting the outcome of cases tried by the European Court of Human Rights based solely on textual content. The resulting model predicted the court's decisions with a 79% accuracy, on average.

Start your evolution

Interested in learning more about the NLP revolution? Read more about how Manz speeds research workflows using deepset Cloud, and get in touch with us via www.deepset.ai