Breaking Down Legal Jargon with NLP and AI Technologies

Today, we’ll dive into how Natural Language Processing (NLP) and Artificial Intelligence (AI) can help companies process legal documents more efficiently.

Legal documents can be complex and lengthy, containing hundreds of pages of text that must be understood to make decisions or act. This is where NLP and AI come in. These technologies can help companies ingest and analyze legal documents quickly and accurately, saving time and money.

The first step in processing legal documents is document ingestion. Companies receive all kinds of documents, including scanned PDFs and other types of files. OCR technology is often used to convert scanned documents into machine-readable text, allowing computers to read and understand the content.

Once the text is extracted, NLP and AI programs are used to analyze the content. These programs generate internal metadata about the document or content, including where paragraphs are delineated, how articles are called out, and how sections are numbered. They may even identify bolded or italicized words for emphasis or other reasons.

One important aspect of analyzing legal documents is understanding the concept of topics. Extracting topics gives the program a starting point for understanding what the document is about. This can also lead to summarizing the document, which can be done in various ways. Programs can either extract specific pieces of text as highlights or abstract the content to create a summary.

Beyond understanding the general concept of the document, NLP and AI can help identify individual entities, such as places, times, and people involved in the legal matter. This information can provide a better understanding of the document and help with decision making. Additionally, programs can extract tables and figures from legal documents, which can be important in understanding the purpose of the document.

After analyzing the content of the legal document, NLP and AI can help with classification and summarization. Classification involves labeling documents based on their characteristics, such as whether they are agreements, lawsuits, or decisions. This process requires a manual effort, as companies must label documents to train AI systems to recognize the characteristics of different types of legal documents.

Summarization involves extracting the most important information from the legal document. Programs can either extract specific text as highlights or create an abstract summary.

In conclusion, NLP and AI can help companies process legal documents more efficiently, allowing them to save time and money. By using OCR technology, analyzing metadata, and extracting entities, tables, and figures, companies can quickly understand the content of legal documents. Classification and summarization further enhance the understanding of the document, making it easier to make informed decisions.