Natural language processing (NLP) is a technological innovation that is driving change in various industries. As machines gain a more complex understanding of language and context, they can perform tasks previously handled by often congested employees. NLP contributes to applications ranging from translation to word processing, and recently technology has invaded more complex industries. For example, the application of natural language processing to legal documents is revolutionizing business legal and procurement services. Document automation systems, on the other hand, typically use some sort of gap-filling mechanism that allows for the creation of a legal document tailored to specific criteria. In some cases, the data needed to generate the document is retrieved via an iterative question-and-answer dialog: a chatbot if you wish. In such circumstances, the document automation system has the same type of interface as a legal advice system. For a recent project, I had to look at how NLP was used in what became known as legal technology. It turns out it`s a densely populated space: A Stanford website lists 1,084 companies that are « changing the way it`s done legally. » When examining such a landscape, it is useful to have a map. Practically, the practice of law is a well-structured activity where point solutions are available for a number of specific tasks faced by a typical law firm. In my opinion, there are five areas of legal activity in which NLP is playing a growing role: artificial intelligence (AI) is changing the way the legal industry operates.

Although the introduction of AI in law is still new, lawyers today have a variety of smart tools at their disposal. One of the most useful AI applications is natural language processing (NLP). In general, a number of organisations position their document automation offerings in the area of access to justice and make tailor-made legal documentation easily accessible to the general public. Two of these examples are A2J Author and HelpSelfLegal. The most innovative platforms can conduct reviews and provide advice in a fraction of the time it would take a lawyer to review. In some cases, a leading contract review and negotiation technology such as LexCheck can return a fully marked contract in just a few minutes, ensuring that all contract terms are clear and free from ambiguous wording. * LEGAL-BERT-BASE is the model referred to in Chalkidis et al. (2020) as LEGAL-BERT-SC; A model formed from scratch in the legal corpora mentioned below, using a newly created vocabulary from a sentence tokenizer formed on the same corpora. One of the most popular and useful applications of NLP in law is legal research. Thorough research is essential for all legal processes, but it also explains why they take so long.

Resolving a bodily injury can take up to three years, which can discourage clients. Of course, the Big Four quickly developed their own « AI-powered » solutions. In July 2018, LexisNexis launched Lexis Analytics, a legal research tool that includes the adoption of machine learning and NLP start-up Ravel Law, among others. More or less at the same time, Thomson Reuters launched WestSearch Plus, a new search engine that claims to use cutting-edge AI. With the help of AI legal research, lawyers can formulate their requests in natural language like a colleague. Instead of typing « noncompete /s (restrictive or illegal) /s long, » a person could type « What is the time frame for non-competitions in New Jersey? » Depending on the context of the query and thousands of other related queries, the program would make predictions about what exactly the attorney wants to find and suggest keywords to fill out the search (for example, by adding « incriminating » and « non-competing »). NLP also saves lawyers time by locating the searcher and directing them to where certain sentences appear in lengthy court decisions or when parts of a search query appear in relation to other terms. This means that lawyers can quickly decide which cases are irrelevant and move on to the next case or dive deeper into cases whose search terms better match the desired search parameters. Everlaw (founded in 2010; Funding of $34.6 million), on the other hand, still seems to take an approach where a first set of departures (they propose 200 documents) must be marked first. Ui features can be key differentiators: Relativity (founded in 2001, funded to the tune of $125 million), formerly known as kCura, also offers a phone app that lets you « code documents on the way to work or on the couch. » Lawyers are also 24/7. Ross Intelligence (founded in 2014, funded to the tune of $13.1 million), which uses IBM Watson, and vLex (founded in 1998, funded by $4 million) with a product called Vincent offer natural language query interfaces so that « you can ask your research questions as if you were talking to another lawyer. » The law has language at its heart, so it`s no surprise that software that works with natural language has long played a role in some areas of advocacy. But in recent years, interest in applying modern techniques to a wider range of problems has increased, so this article explores how natural language processing is used in the legal sector today.

DoNotPay was created by Joshua Browder, a Stanford student, in response to his own parking experiences. But law firms are also interested in offering legal advice systems. Automation here has clear advantages in providing legal services to those who otherwise would not be able to afford it or would be willing to pay for it. Today, the battle for market share is based on optimized techniques to categorize as quickly and efficiently as possible if documents are relevant. This process is referred to as « technology-based verification » (« ART ») and has been the subject of activities within the TREC legal stream for several years. As with legal research, traditional approaches included keyword or Boolean search, followed by manual review. More modern approaches use machine learning for document classification, which is known as « predictive coding » in the legal profession. You want to maximize both accuracy and memory while keeping the effort (in terms of how many documents a person needs to comment on or review) at an appropriate level. In the legal community, there is some debate about the advantages and disadvantages of different techniques, in particular what is considered a reasonable seed set and whether passive or active learning is preferable, the former involving a random selection of documents for human marking and the latter a deliberate selection by machine of uncertain or supposedly relevant examples.

(See here and here.) 12,554 cases of HUDOC, the depositary of the European Court of Human Rights (ECHR) (hudoc.echr.coe.int/eng). It is difficult to overestimate the meaning of word choice and syntax in the law. Any inaccuracy in a contract or other legal document may open it to unintentional interpretations. Natural language processing can help lawyers avoid these mistakes in document creation and protect their clients and reputation. EDiscovery, or eDiscovery, is the process of identifying and collecting electronically stored information in response to a request to be created in connection with litigation or investigation. Given the hundreds of thousands of files that can reside on a typical hard drive, a key issue here is separating that content into what`s relevant (or « responsive, » in domain terminology) and what`s not. In a case involving a recent patent litigation with Apple, Samsung collected and processed approximately 3.6 TB, or 11,108,653 documents; The cost of processing this evidence over a 20-month period was more than $13 million. Because these NLP programs are based on machine learning, the more lawyers use them, the more useful they become. With more cases to analyze, these predictive models become more accurate. LexCheck uses natural language processing to perform legal document checks that ensure stricter and less ambiguous contracts. To see how it works, request a demo or contact us by email at sales@lexcheck.com. NLP learns human language, uses context and previous queries and results to predict what lawyers will need in their research.

A clear example of NLP is the use of Google Search. For example, if a user starts typing « restaurant, » Google can automatically suggest « restaurants near me. » The more the user searches on Google, the more Google can predict what the user is looking for when they « Stayed… If the user misspelles « Restaurants near me, » Google detects the misspelling and returns the correct search results. The same goes for AI in legal research. Similar to Google, NLP improves legal search results as lawyers use online search tools. Here are some ways in which AI legal research streamlines and simplifies legal research. Lawyers who work smarter and more efficiently also have a positive impact on their clients. AI legal research allows lawyers to provide their clients with work products that are completed more accurately and quickly without increasing legal fees accordingly. As mentioned earlier, in traditional online legal research, not creating a sufficiently accurate set of search terms is crucial to getting the right information.