Question Answering

- Overview 

Question Answering (QA) in NLP is a system that enables a computer to understand questions posed in natural language and provide relevant answers from a given text or knowledge base. 

These systems use techniques to analyze the question, locate the answer within a context, and present it in a concise, readable format. 

This allows for applications like chatbots and search engines that can understand user queries and provide direct information, rather than just a list of links.

1. How Question Answering works:

• Understanding the question: The system first analyzes the user's query to understand its intent and extract key information.
• Processing information: It then processes a collection of text, which can be a large document, a knowledge base, or a set of unstructured data.
• Retrieving the answer: The system uses algorithms to find the most relevant passage or fact within the data that answers the question. This often involves removing stop words, stemming words to their root form, and tagging key entities.
• Generating a response: Finally, the system presents the answer in a natural language format that is easy for the user to understand.

2. Types of Question Answering: 

• Factoid QA: Designed to answer questions that can be answered with a single fact, such as "What is the capital of France?"
• List QA: Answers questions that require a list of facts, like "What are the countries in the European Union?"
• Definition QA: Provides definitions for a term or concept, for example, "What is machine learning?"
• Scenario QA: Answers questions that require a narrative or explanation, such as "What would happen if...?"
• Extractive vs. Generative: Some systems extract a direct span of text from the context (extractive), while others generate a new response based on information from multiple sources (generative).