Legal documents can be complicated and time-consuming to process, but with the advancement of Natural Language Processing (NLP) and Artificial Intelligence (AI), companies have been able to work with them more efficiently. In this article, we will delve into how legal document processing works using NLP and AI.

The first step in legal document processing is document ingestion, where various types of documents are ingested, including PDFs, scanned documents, and more. Optical Character Recognition (OCR) is used to convert the printed text on these documents into computer-readable text, often in the form of a PDF. After that, the content of the document is analyzed by a program using NLP and AI.

One of the essential elements in legal document processing is metadata. Metadata is generated about the document itself and helps to classify the document by identifying how articles are called out, how sections are numbered, where paragraphs are delineated, and what content is emphasized. Additionally, NLP and AI programs can understand the document’s topics, which can lead to a summary of the document.

The summary can be achieved through two methods: extraction and abstraction. Extracting the summary involves taking small pieces of text from the document and using them as highlights, while abstraction involves a deeper understanding of the document’s content and summarizing it. Once the document’s topics are understood, and a summary is extracted or abstracted, the next step is to identify the individual entities within the document.

Entities include places, times, people, and other elements that help us to understand the document better. The program uses NLP and AI to extract the entities and identify tables within the document. Figures and tables in legal documents can be essential in understanding the document, and the program will help to identify and understand their purpose.

The last step in legal document processing is the extraction of clauses or articles. This step helps to further understand the document and summarize it more effectively. The focus throughout this process is on classification, topic extraction, and summarization. The program will use the metadata and extracted entities to classify the document and extract topics, which will help to create a summary. Table extractions and clause or article extractions are also essential in understanding the document better.

In conclusion, NLP and AI have revolutionized legal document processing, making it faster and more efficient. By using OCR to convert printed text into computer-readable text, programs can analyze and extract information from legal documents. The focus is on classification, topic extraction, and summarization, with metadata, entity extraction, and table extraction being important elements of the process. Legal document processing has been made easier, and companies can now spend more time on other essential tasks.