Zero-shot information extraction

[likhon450@]

More and more companies are realizing the value of artificial intelligence and are eager to implement these technologies in their companies. The combination of intuition and artificial intelligence-driven analytics has great potential to benefit companies in increasing knowledge and making better decisions. Against the background of users being able to interact with information more effectively, track it, identify, understand and, most importantly, act on it. The integration of natural language processing (NLP) and automated analytics will allow people to interact with information.


Large Language Models (LLM) allow us to extract personally identifiable information (such as name, SSN, email address, phone number, date of birth) from unstructured textual information into a structured form. This canV be used to better organize information within an organization, such as in the case of email, or to anonymize various documents. Invoice Extraction: Extracting information from documents, especially invoices and forms, using a combination of Optical Character Recognition (OCR) + Large Language Models (LLM) and even without OCR.


Typically, there are three subtasks that can be used separately: Document format analysis: Dividing or segmenting a document into coherent parts, for example, multi-line addresses, multi-paragraph paragraphs, tables, or items within tables. Interval classification: Identifying text by various types - addresses, quantities, phone numbers, company names, numbers, etc. Relational Extraction: The most promising future approach to zero-shot semantic relation extraction is that it allows us to identify relationships between textual intervals in a document without requiring additional work.