QA overview

QA systems are designed to understand natural language queries and provide relevant answers based on a given context or knowledge source. These systems leverage advanced NLP techniques and machine learning models to comprehend the intent behind a user’s question, extracting the most appropriate response from the available information.

Let’s consider an example scenario of a typical QA system: suppose you are a content writer for a technology company and you need to explain the concept of optical character recognition (OCR) to your audience. A QA system could assist you in this task by providing relevant information from its knowledge base, or by analyzing a given text corpus related to OCR.

Here’s how a QA system might handle a query such as What is optical character recognition used for?:

• Query understanding: The system first analyzes the query to understand the user’s intent and extract key entities and concepts. In this case...