Enhancing the Legal Industry With NLP
Imagine that as a lawyer, you’re able to read hundreds of pages of contracts and cases, just to filter and find keywords, and sentences. Now think again, but within milliseconds. Computers powered by NLP can analyze and interpret thousands of written language papers and filter out whatever information you need.
A study from 2016 found that by using NLP and machine learning, researchers could be 79% accurate on how the European Court of Human Rights would rule on a case. To have strong predictions as such, you need to have a solid understanding of how court rulings work, and how arguments can be tailored based on the situation.
In these types of circumstances, AI assistance isn’t always necessary; but when you scale it to a higher level, thousands of decisions must be made, and the use of technology helps keep efficiency.
Conventional Online Databases
The research skills you need when you are a lawyer are unmatched. Searches for specific information could take days, and you’d still not be able to find complete results or the results that you need.
As a lawyer, researching for projects and cases pushes you to your limits and sometimes towards loopholes. The more cases to read through and analyze, the more time wasted for the lawyer, and money wasted for the client.
How can NLP be directed to improve the capability of lawyers around the world, while still keeping high standards?
These are the main points I'll be covering:
- The Components of Natural Language Processes
- How NLP’s play a role in the Legal Industry
- What current software can do + the future
Breaking Down NLP
Natural Language Processing (NLP) gives computers the ability to understand the text and spoken words in the same way the human mind can. It reads information, breaks it down, understands it, and makes decisions to respond.
NLP can be divided into 2 main subcategories:
- Natural Language Understanding (NLU)
- Natural Language Generation (NLG)
Think about it as a website where reports of the share market are posted every day. For this task, you need people to research and collect data, create reports, and post them. This is boring and time-consuming.
Unless NLP, NLU, and NLG are used.
In this case, NLU and NLP can understand the share market’s data and break it down, allowing the NLG to generate a post for the website. As a result, human labor is removed and productivity levels are drastically increased.
Natural Language Understanding (NLU)
NLU helps the machine to understand the data. It is used to interpret data to understand the meaning of data to be processed accordingly. It solves it by understanding the context, semantic, syntax, intent, and sentiment of the text. For this purpose, various rules, techniques, and models are used. NLU converts text into a machine-readable format.
There are three different linguistic levels for understanding language (as shown in the diagram earlier):
Lexical Ambiguity
The ambiguity of a single word is called lexical ambiguity. As an example, treating the word fast as a noun, an adjective, or a verb.
→ Ambiguity can be referred to as the ability for a word or statement to have more than one meaning, depending on the context.
How do we bypass this?
Word Sense Disambiguation (WSD); where the technique aims at automatically assigning the meaning of the word in the context in a computational manner.
Automatically identifying the intended sense of ambiguous words improves the performance of biomedical and clinical applications such as medical coding and indexing; applications that are becoming essential tasks due to the growing amount of information available to researchers.
here’s a research paper I found interesting, diving deeper into WSD.
Syntactic Ambiguity
This level is unexpectedly vague and does not follow the reading ‘rules’; Improper pauses at wrong times, rambling and incomplete sentences.
It’s the fact that you can interpret a sentence in more than one way.
In this example, the sentence is puzzling in terms of the subject, unclear on who/what the chicken is, causing confusion.
Referential Ambiguity
This ambiguity occurs when a word or phrase could be referring to two or more properties or things. In the example below, if more text and context for the subject were given, the computer’s confusion would’ve drastically decreased.
The more context given, the less ambiguity-related challenges.
Natural Language Generation (NLG)
It's the process of making datasets understandable and automating the process of writing in natural language.
NLG is a multi-stage process, but the 3 main components include:
Text Planning
→ This is where the information that should be communicated to the user is decided, and how it should be structured.
Sentence Planning
→ Deciding how the information will be split amongst sentences and paragraphs, and what devices (eg. nouns, verbs) should be added to context for the text to flow smoothly.
Text Realization
→ Last step, generating the sentences in a grammatically correct procedure.
A great example of NLG is automated reporting, where a computer is set to search the internet and scrape data from various sources to make a summary of its findings.
tl;dr.
NLP (Natural Language Processing): It understands the text’s meaning.
NLU (Natural Language Understanding): Whole processes such as decisions and actions are taken by NLP.
NLG (Natural Language Generation): It generates the human language text from structured data generated by the system to respond.
NLP In Legal Tech
While AI adoption is still new, lawyers today have a range of tools on hand to help them with tasks such as researching.
But the implementation of NLP and AI in the legal line of work isn’t new.
One of the earliest systems for an online legal database first appeared in the 1960s and 1970s. Legal reading systems were huge topics of discussion in the 1970s and 1980s, but the last few years have seen a significant upsurge of interest.
With the power of NLP, legal professionals can:
- Structure legal information
- Build winning strategies in cases
- Save time, money, and relevance to optimize the relationship with their plaintiff.
There are many use cases for NLP in law, but these are some of the major ones:
Creating Virtual Data Rooms
Just as any law firm has a copy room full of records, data, and files that they've used in the past or will need for future cases, creating a virtual data room will allow for secure storage space, where the firm can provide its users with controlled access over documents
For example, corporate attorneys can use the virtual data room for mergers and acquisitions transactions.
In terms of real estate, online document repositories would allow lawyers to easily manage their listing documents, sales/leases, loans, and mortgages.
Organization is another huge advantage.
Lawyers can use these data rooms to arrange files under a project or case. This will allow for better efficiency, functionality, and privacy. Therefore, creating the ideal place to exchange information with opposing counsel, experts or colleagues, or even your clients.
Document Automation and Providing Legal Advice
These two applications are not quite distinctive from each other, so let's consider them as one and merge them.
Document Automation Systems use an operation that allows for the creation of a legal document under a guideline.
Legal Advisors are systems consisting of a set of questions that produce advice specifically tailored to that case or situation.
Legal Case Law Research
Online legal databases have been out for decades, but NLP can drastically boost the entire process; let me break it down:
Conducting thorough research is essential for all legal processes. Personal injury claims can take up to 3 years to just process
NLP can help shorten these times by translating research into the legal language (yes, this is a thing!), making it easier and faster to examine documents. While this is just basic, more complicated NLP programs can filter for concepts as well, directly assisting lawyers (ex. specific regulations).
This is an active example of an NLP at work, sifting through a legal document. It knows that certain texts are Addresses, Case Names, Citations, etc.
Some of these programs can process documents in 20 languages, thus creating a system where lawyers across the world are able to benefit from, and decrease the number of problems and data risks.
How lawyers are using AI today: NLP can Convert English Search Terms into Legal Searches
- Using AI legal research, attorneys can frame their questions the same as they would to a colleague using natural language.
- Instead of typing keywords to a case, a person could type in a question, and based on the context, thousands of other related questions, and the program would make predictions as to what you want to find + suggesting keywords.
- The program continues to learn based on what cases are clicked on and reviewed. If it seems that all the cases using specific phrases are ignored, it will narrow and adjust the search results.
Point is, NLP plays a huge role in today’s legal industry. Legal work can rarely be straightforward, making the experience frustrating for both the lawyer and their clients. NLP and other similar tools are just one of the many ways to improve the process of research, automation, and even data rooms.
While this concept is fairly new and in its early stages with large law firms, the potential that it has for the future of law and people is unreal. These algorithms can predict how a court may rule, even helping lawyers modify to create a more effective argument, I can’t wait to see what NLP can bring us in the near future, and how the world’s largest firms can develop into “smart” law firms.
If you found this article interesting or learned something new, I’d love to connect with you on LinkedIn. Also, if you’d like to stay updated on what I’m up to, you can subscribe to my monthly newsletter here!
