Dear in-house lawyer or legal professional, in this article you will find everything you need to know about named entity recognition with AI (Artificial Intelligence). How many times have you found yourself drowning in files and surrounded by emails that make you avoid your inbox? In the business ecosystem, managing large amounts of information efficiently is critical to success. Enter AI-powered entity recognition, a technology that is transforming the way lawyers handle and analyse legal documents. Curious about what it can do for you? Read on to find out.
AI is redefining the legal landscape by offering a faster, more accurate and efficient way to identify and extract key information. In this article, we take a closer look at what AI entity recognition is, how it can benefit the legal department and the advantages it offers for automating repetitive tasks. Get ready to discover how this technology is changing the legal game, and how it can help you take your in-house legal practice to the next level.
Named Entity Recognition (NER) is a process by which artificial intelligence systems identify and extract specific entities, ranging from dates, names of parties, prices or competent courts, to matters to be considered by the parties to a contract (such as an assignment of rights), within a text or dataset.
It is a technology with many applications in the legal field.
Using advanced natural language processing (NLP) algorithms, AI analyses the context and structure of text to automatically and accurately recognise and label these entities. This technology has many applications in the legal field, from extracting key information from contracts and legal documents to analysing large volumes of data to identify patterns and trends relevant to legal cases and business decisions.
NER is based on an approach known as discriminative AI, a machine learning technique that identifies specific patterns in unstructured data, such as legal documents. It uses mathematical models trained on large labelled datasets to distinguish between different classes of entities, such as party names, dates and contract terms. These models are applied to new documents to automatically identify entities of interest, enabling accurate recognition.
In short, it is a hawk's eye that saves hours of contract analysis, a giant magnifying glass that automatically targets the parties you want to identify, and detects key metadata for the legal department. Let's look at some of the key applications of this technology for your legal department:
Entity recognition can analyse legal documents in PDF format and automatically extract metadata such as party names, dates, locations and specific contract terms. This streamlines the process of reviewing and analysing legal documents, allowing lawyers to quickly access key information without having to review each page manually.
Using AI entity recognition, document automation systems can identify and analyse specific variables and conditions in contracts and other legal documents. For example, they can identify indemnity clauses, payment terms or default conditions, facilitating the creation of templates and automated workflows for drafting and reviewing legal documents.
Entity recognition can also help centralise and organise key data in a centralised repository. By automatically identifying and tagging relevant information in legal documents, such as party names, expiration dates, and contract terms, AI systems, hand in hand with the right legal technology, can help build a structured and accessible database for the legal department. This makes it easier to search and retrieve information, and to analyse data to inform decision-making.
Having read about the direct applications of named Named Entity Recognition in the legal department, you may be thinking about how it can be used in your most repetitive and manual daily tasks. Let's talk about two of the main benefits this technology can offer you.
Those legal document review and analysis tasks that used to take hours of manual work can now be performed automatically and efficiently. In the past, your legal team spent a large part of their day poring over every comma in hundreds of pages of contracts, looking for specifics. Now, by uploading a contract to a Contract Lifecycle Management (CLM) platform that integrates this type of AI, you can automatically identify and extract this data, allowing you to process large volumes of documents in much less time. It's time to free your lawyers from repetitive tasks - this technology allows them to focus on more strategic, higher-value activities.
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As well as saving time, this technology also reduces the risk of overlooking critical details in legal documents. By eliminating the time spent on manual review, the risk of making mistakes or overlooking critical details is minimised, helping to improve the quality and accuracy of the legal department's work.
Discriminative AI, as well as generative AI, is a very powerful tool on its own, but it must be taken into account that it will not go beyond the generic unless it is fed with the right information and by the most experienced professionals in a specific area, such as the legal field. Once this is achieved, AI has a great impact when applied to specific technologies, as it is an ideal complement that can become an assistant, or a copilot, as Microsoft has well understood. In the case of CLM, AI, once integrated with this technology, is an extra that contributes to automation and data security that will change the way legal departments work for the better.
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