What Exactly Is a Question Answering System?

While the term "question answering system" might seem straightforward, it's important to clarify how these systems differ from standard search engines like Google. The Internet serves as a vast reservoir of information today, and many people depend on it for answers. Typically, users turn to search engines to find the information they need. However, what do they actually obtain from the search results? How much time does it take to pinpoint the specific answer they seek?

For instance, consider this query in Google: “Is vaccination required to fly in Europe?” The answer can be elusive due to the rapidly changing regulations surrounding COVID-19. Nonetheless, if you are vaccinated, as of November 21, 2021, you can fly within Europe. Yet, when we look at Google's results page:

It becomes apparent that Google can’t guarantee a definitive answer. Instead, it presents a range of links, primarily from reputable sources like the European Union and Forbes, allowing users to choose which to explore. The downside is that users often must sift through multiple documents to find the desired information, a process that can be both time-consuming and frustrating.

For someone without specialized knowledge, navigating through these sources to extract precise information can be quite challenging and may lead to uncertainty regarding the results' accuracy. This is where automated question answering systems come into play.

An automated question answering system searches the Internet based on user inquiries and compiles relevant information. Utilizing syntactic and semantic analysis, these systems provide answers, frequently in near real-time. This technology represents a significant advancement in Natural Language Processing (NLP) and machine learning.

P.S.: If you're new to NLP, I recommend checking out my previous post that outlines how to begin your journey into this fascinating field:

Learning Track to Master Natural Language Processing (NLP)

In this article, I will address common questions, including how to get started in NLP and the essential techniques to learn.

1. Factoid Questions

Factoid questions are typically answered with brief phrases or single sentences. These include “Wh”-questions such as what, when, where, why, and how. For example, “Where was Gautam Buddha born?”

2. Confirmation Questions

Confirmation questions yield yes or no responses. An example is, “Is time travel faster than light possible?” Answering these requires complex reasoning, general knowledge, and inference, especially for subjective queries.

3. Listing Questions

Listing questions require answers in the form of lists of entities or facts. For instance, “What are the counties in Georgia?” Responses can range from simple lists to comprehensive tables.

4. Descriptive Questions

Descriptive questions often begin with “why” or “how.” Answers can be detailed or concise, ranging from a single sentence to an entire document. For example, “Why is the sky blue?”

5. Hypothetical Questions

Hypothetical questions relate to imagined scenarios and often start with phrases like “What would happen if” or “What would have happened if.” For example, “What would have occurred if British America had not revolted?”

6. Complex Questions

Complex questions typically do not fit into the previous five categories. They often require advanced reasoning and synthesis of information from multiple sources. An example is, “What causes a volcano?”

Final Thoughts

These are the types of questions we commonly pose on the Internet. At times, we seek straightforward answers (e.g., “What is the capital of Poland?”), while at other times, we desire more elaborate explanations (e.g., “What is the relationship between vaccinations and COVID-19 hospitalization rates?”).

This first video delves into question answering systems, discussing their role in NLP and showcasing relevant examples.

The second video provides an introduction to question answering systems, covering the various types of questions and their applications in NLP.