Building a Question Answering System Using Natural Language Processing (NLP)

Applied to Natural Language Processing (NLP), Question Answering (QA) systems are considered an applied NLP. Stated differently, the goal for these systems is to provide automatic responses to questions asked by humans in natural language form. Unlike traditional information retrieval systems that return a list of documents, QA systems strive to return specific answers to user questions. This document will explore how QA systems work, their architecture, and techniques used, and it will demonstrate two project examples: a Closed-Domain QA System and an Open-Domain QA System.

Understanding QA Systems

This is a difficult task, as it combines multiple intricate aspects of NLP, such as question understanding, retrieval of pertinent information, and generation or selection of the right answer. QA systems are generally divided into the following:

Closed-Domain QA: Designed for specific domains (e.g., medical, legal), these systems work on a predefined dataset and deliver more accurate results within that domain.

Open-Domain QA: These systems can answer questions from any domain, usually retrieving answers from a large corpus such as Wikipedia.

Applications of QA Systems

Customer Service: Automate responses to frequent customer queries.

Education: Serve as tutoring assistants, answering students' questions.

Healthcare: Help patients understand symptoms and conditions.

Legal and Financial: Quickly retrieve facts from lengthy documents.

Enterprise Knowledge Management: Employees can ask questions related to company policies, benefits, or technical documentation.

Search Engines: Power the direct answers you see on platforms like Google when you type a question.

Core Components of QA Systems

Question Processing

Classify the question type (who, what, when, etc.)

Extract key information (named entities, nouns, verbs)

Determine the intent of the question

Document Retrieval

Retrieve documents or passages potentially containing the answer

Use search algorithms like TF-IDF, BM25, or transformer-based models like Dense Passage Retrieval (DPR)

Implement keyword expansion and semantic search for better coverage

Answer Extraction or Generation

Span extraction (e.g., with BERT or RoBERTa)

Generative models (e.g., T5, GPT) for longer or abstractive answers

Answer ranking and confidence scoring

Post-Processing

Filtering or ranking answers

Formatting the output for user understanding

Providing source references or snippets

Popular Models and Libraries

Transformers: BERT, RoBERTa, DistilBERT, GPT, T5, ALBERT

Libraries: Hugging Face Transformers, Haystack, spaCy, NLTK, PyTorch, TensorFlow

Evaluation Metrics

Exact Match (EM): Checks if the returned answer exactly matches the correct answer.

F1 Score: Measures overlap between predicted and actual answers.

BLEU/ROUGE: For generative QA, these compare n-gram overlaps.

Mean Reciprocal Rank (MRR): Useful in ranking-based QA.

Precision and Recall: To evaluate how many correct answers were retrieved and how relevant they were.

Project Example 1: Closed-Domain QA System (Medical QA Bot)

Objective: Create a QA system that can answer medical-related questions using a curated dataset of medical FAQs.

Dataset: Publicly available medical FAQs or the MedQuAD dataset.

Tech Stack:

Python

Hugging Face Transformers

BERT or BioBERT (pretrained on medical text)

Flask for API

SQLite for storing FAQs

Steps:

Data Preparation

Clean and preprocess the FAQs (remove stop words, tokenize).

Convert them into question-answer pairs.

Store data in a structured format such as JSON or SQLite.

Fine-Tune BERT

Fine-tune BERT/BioBERT on the dataset to adapt it to the medical language.

Use QA-specific fine-tuning scripts from Hugging Face.

Build Retriever and Reader Modules

Use TF-IDF or BM25 for document retrieval.

Implement the reader using a BERT-based model to extract answer spans from top passages.

API Interface

Build a Flask API that can receive questions, fetch answers, and send back the result in a JSON format.

Use Case: A patient may query, "What do the symptoms of diabetes feel like?" and will respond with a succinct answer like “Increased thirst, Increased urination, Blurred vision”.

Challenges:

Ambiguity in questions

Context sensitivity (different answers for similar questions)

Privacy and ethical considerations for medical data

Ensuring compliance with healthcare regulations like HIPAA

Project Example 2: Open-Domain QA System (Wikipedia QA System)

Objective: Build a QA system that answers general knowledge questions using Wikipedia articles.

Dataset: Wikipedia dump or use an API like Wikipedia API for retrieval.

Tech Stack:

Python

Haystack or Transformer

DPR for retrieval

BERT or RoBERTa for reading

FAISSfor dense indexing

Streamlit for UI

Steps:

Data Indexing

Download the Wikipedia dump or give its access by API.

Preprocess documents (clean text, chunking).

Use FAISS to create a vector store for dense passage retrieval.

Retriever Setup

Use Dense Passage Retrieval to encode questions and passages into vectors.

Retrieve top-k passages based on similarity.

Reader Setup

Load a pretrained QA model (like RoBERTa) to extract answer spans from top passages.

Use fine-tuning the reader on a QA dataset like SQuAD.

Build Frontend

Develop a simple and interactive UI using Streamlit.

Take user input and display sources.

Use Case: The user asks, “Who invented the telephone?” and the system answers, "Alexander Graham Bell," along with a snippet and source link.

Challenges:

Real-time performance

Large memory requirements for indexing and retrieval

Keeping the Wikipedia data up to date

Handling ambiguous or open-ended questions

Future Directions in QA Systems

Multilingual QA: Expanding systems to handle multiple languages.

Multimodal QA: Integrating text, images, and videos.

Explainable QA: Providing reasons or sources for the answers.

Edge QA Systems: Deploying lightweight models on mobile/IoT devices.

Hybrid QA Approaches: Combining retrieval-based and generative models.

QA for Code: Answering programming-related queries using code documentation and forums.

Conclusion

Constructing a QA system based on NLP is a rather complex process that comprises at least three stages of natural language understanding: question analysis, document retrieving and answer generation. No matter if you are building a domain-centric application or a general-purpose bot, the basics of natural language processing never go out of style. Already, using robust tooling such as Hugging Face and BERT models, developers can build QA systems that beat humans on certain tasks.

These systems have the potential to change the way humans interact with knowledge and information across industries, including healthcare, education, customer service, and even entertainment. With the development of the field, we shall see more intelligent, conversational QA systems that seamlessly integrate into our digital lives.

In the end, building a QA system is a great chance for you to dive into NLP in general, broaden your knowledge of transformer models, and develop solutions that can be used in any industry.