Nlp in Legal Tech

An understanding of how a court is likely to decide can help lawyers better adjust their arguments to support or combat the prediction. This happens all the time when you try to appeal to the median Supreme Court justice, who is likely to be the deciding voter. Lawyers can do this job even with the Supreme Court, where the law is relatively small for a particular judge on a particular topic and AI support isn`t always needed. But this is not always the case. For example, if you`re going to a lower court in New York, where a judge can make thousands of decisions, the use of technology helps streamline and quickly identify relevant cases for analysis. NLP is a subset of AI that processes natural human language, whether in text or voice. Some of the most well-known examples include Google`s predictive search suggestions, spell checkers, and speech recognition. NLP is a promising industry that is expected to be worth $27.6 billion by 2026 and has a significant impact on the legal sector. A study by the National Legal Research Group, Inc. found that AI tools allowed experienced legal researchers to complete their searches 24.5% faster than traditional legal research, saving between 132 and 210 hours per year. These extra hours can be spent designing, reviewing, and managing cases at a higher level, tasks that computers can`t do. There is a blurred line between document automation systems and legal aid applications, so I will look at the two categories together.

Law has language at its core, so it`s no surprise that software that works with natural language has long played a role in some areas of the legal profession. But in recent years, interest in applying modern techniques to a wider range of problems has grown, so this article explores how natural language processing is used in the legal sector today. The most publicly visible legal advisor is DoNotPay, an interactive tool that was originally intended to help the public appeal parking tickets. The scope has expanded considerably since then; At the time of writing, the DoNotPay app supports 14 different use cases, including fighting unfair bank fees, credit card and overdraft fees, refunds from Uber and Lyft if a driver makes a wrong turn, and requesting a refund for late package deliveries. NLP has already penetrated the legal field, let it really be enough for the job. The most common application for NLP in law is document review and management, where AI algorithms analyze and highlight relevant information. 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 acquisition of machine learning and NLP startup Ravel Law, among others.

Around the same time, Thomson Reuters launched WestSearch Plus, a new search engine that claims to use cutting-edge AI. 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. Every aspiring lawyer has learned to search online databases. These legal research skills need to be taught because they are not intuitive. Let us refer to the above example of a non-competition clause. Suppose a lawyer has to determine how long a non-competition clause can last before a court finds the clause too restrictive. According to traditional search methods, they should create a search query such as „non-competitive (restrictive or illegal)/s long(s)”. This search may or may not return full results. For example, if a court finds that the non-compete obligation was „onerous” and not „restrictive”, that case would not be included in the search results.

If the decision refers to the clause as a „non-compete obligation” and not as a non-compete obligation, the case cannot be included in the results. This research is also likely to attract many more cases than is relevant. For example, this query may list one case that says, „Restrictive non-compete obligations have long been a staple of employment contracts” – and probably hundreds more. This means more cases to be sought at the expense of the lawyer`s time and client funds. The application of natural language processing and artificial intelligence in general in the legal profession is not new. The first online legal content search systems appeared in the 1960s and 1970s, and legal expert systems were a hot topic of discussion in the 1970s and 1980s (see, for example, Richard Susskind`s Expert Systems in Law, Oxford University Press, 1987). In recent years, however, interest in this field has increased significantly, including, predictably, a growing number of startups claiming to apply deep learning techniques in the context of specific legal applications. DoNotPay was developed by Stanford student Joshua Browder in response to his own experience with parking tickets.

But law firms are also interested in offering legal advice systems. Automation has obvious advantages here, as it provides legal services to those who otherwise could not afford it or would be willing to pay for it. Contract review may be carried out at the level of the individual contract or, for example in the case of due diligence for a business acquisition, may involve the review of thousands of contracts that are on file. In the latter case, the technology is also beginning to integrate aspects of what`s known as legal analysis, aggregate information into the dataset to detect anomalies and outliers, and create graphs or tables that make it easier to compare documents. Ambiguity is an important issue in legal documents. This can limit the protective measures provided, change the conditions and create confusion for all concerned. Often, lawyers may overlook these issues because they do not recognize ambiguities in the wording of the contract they are drafting or reviewing. Just as people may assume that „I haven`t slept in three days” means that someone suffers from insomnia, a lawyer may believe that the meaning of his clause or amendment is clear to the reader and can only have a reasonable meaning. This is especially true if the lawyer has to review a contract that spans hundreds of pages and contains complex terms and agreements. NLP can contribute to this review by identifying ambiguities and suggesting revisions for improvement.

Legal research is the process of finding information needed to support legal decision-making. In practice, this usually means sifting through both the law (as created by the legislature) and case law (as developed by the courts) to determine what is relevant to a particular issue. Probably the largest player in this field is Exterro (founded in 2004, funded to the tune of $100 million; The Exterro blog is a useful source of information on e-discovery). Their latest technology, called smart labeling, saves users from having to provide initial sets of human-labeled documents and select the most relevant documents to review early in the review process. DISCO (founded in 2012, funded to the tune of $50.6 million) offers a similar deep learning-based solution in its „priority review” process. For a recent project, I needed to check how NLP was used in what became known as Legal Tech. It turns out it`s a densely populated space: a website at Stanford lists 1,084 businesses that are „changing the way it`s done legally.” When examining such a landscape, it is useful to have a map. Practically, legal practice 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 practice where NLP is playing an increasing role: A 2018 survey found that 59% of legal clients expect lawyers to be available outside of office hours. Chatbots can provide 24-hour support and help these clients get the answers they need without lawyers having to work overtime. These same services could also answer people`s initial questions and help them access the services they need faster.

A less complex application of NLP in legal work is chatbot support. They can`t offer legal advice, but they can learn and categorize clients` needs before passing them on to a human representative. Natural language processing can help shorten these periods by streamlining the search process. NLP legal search engines can translate simple language into „legal language”, making it easier to find relevant documents and cases.