Building a Simple Text Generator: A Hands-on Introduction

Introduction

Text generation is one of the most exciting applications of Natural Language Processing (NLP). From autocorrect and chatbots to AI-generated stories and news articles, text generation models help machines produce human-like text.

In this blog post, we’ll introduce a simple yet effective text generation method using Markov Chains. Unlike deep learning models like GPT, this approach doesn’t require complex neural networks—it relies on probability-based word transitions to create text.

We’ll walk through:
✅ The concept of Markov Chains and how they apply to text generation.
✅ A step-by-step implementation, fetching Wikipedia text and training a basic text generator.
✅ Example outputs and future improvements.

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The Concept of Markov Chains in Text Generation

A Markov Chain is a probabilistic model that predicts future states (or words) based only on the current state (or word), rather than the full sentence history.

How it works in text generation:

1️⃣ We analyze a given text to determine which words commonly follow others.
2️⃣ The model stores these relationships in a transition graph.
3️⃣ When generating text, the model predicts the next word based on probability, choosing words that frequently follow the given word in the training data.

🧠 Key idea: Instead of "understanding" language, the model simply learns word patterns and uses them to generate text that mimics the structure and style of the source.

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Implementing a Simple Text Generator

We’ve built a basic text generation system that follows these three steps:

1️⃣ Fetch Wikipedia Text: Retrieve text from a Wikipedia page using the wikipedia-api library.
2️⃣ Train a Markov Chain Model: Tokenize the text into words and build a transition graph.
3️⃣ Generate Text: Use the trained model to generate text, starting from a given word.

Let’s break it down.

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Step 1: Fetching Wikipedia Text

To gather input data, we use the wikipedia-api library. The script fetches a Wikipedia page and saves its text to a file for later processing.

python

import wikipediaapi

def get_wikipedia_text(title, lang="en"):
    wiki = wikipediaapi.Wikipedia(language=lang)
    page = wiki.page(title)
    return page.text if page.exists() else None

Example usage:

python

text = get_wikipedia_text("A. P. J. Abdul Kalam")

This fetches the Wikipedia page content of Dr. A. P. J. Abdul Kalam.

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Step 2: Training a Markov Chain Model

The model processes the text by splitting it into words and recording which words commonly follow others. This forms the basis for predicting word sequences.

python

from collections import defaultdict
import random

class SimpleTextGenerator:
    def __init__(self):
        self.graph = defaultdict(list)

    def train(self, text):
        tokens = text.split()
        for i in range(len(tokens) - 1):
            self.graph[tokens[i]].append(tokens[i + 1])

💡 How it works:

• The train() method builds a dictionary where each word points to a list of words that follow it in the source text.
• This transition graph captures the statistical relationships between words.

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Step 3: Generating Text

Once trained, the model generates text by selecting the next word probabilistically based on the transition graph.

python

def generate(self, prompt, length=10):
    words = prompt.split()
    current_word = words[-1]
    output = words[:]
    
    for _ in range(length):
        next_words = self.graph.get(current_word, [])
        if not next_words:
            break
        current_word = random.choice(next_words)
        output.append(current_word)
    
    return " ".join(output)

📝 Example usage:

python

model = SimpleTextGenerator()
model.train(text)  
generated_text = model.generate("Dr. A. P. J. Abdul Kalam", length=20)
print(generated_text)

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Sample Output

Given the starting phrase "Dr. A. P. J. Abdul Kalam", the output might look like this:

👉 "Dr. A. P. J. Abdul Kalam was an Indian scientist and politician who served as the 11th President of India from 2002 to 2007. His contributions to..."

🔍 While the text doesn't have deep understanding, it mimics the structure of the original Wikipedia text by following learned word sequences.

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Limitations & Future Improvements

This proof-of-concept demonstrates the basics of text generation, but it has limitations:
❌ It doesn’t understand grammar or meaning—it just predicts the next word probabilistically.
❌ Coherence decreases over longer outputs.
❌ Repetitive sequences may appear due to simple word transitions.

Ways to Improve It:

✅ Use bigrams or trigrams (considering word pairs or triples for better context).
✅ Assign probabilities to word transitions instead of pure randomness.
✅ Extend the model to more advanced NLP techniques (like Recurrent Neural Networks or Transformers).

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Conclusion

This simple Markov Chain model gives us a great starting point for text generation. While basic, it shows how text patterns can be learned and reproduced without deep learning.

🚀 Next Steps:
We will explore more advanced text generation techniques, including:
🔹 Recurrent Neural Networks (RNNs) & Long Short-Term Memory (LSTMs)
🔹 Transformers (GPT, BERT, etc.)
🔹 Comparing different text generation approaches

👨‍💻 Check out the full code on GitHub:
👉 [GitHub Repository]

Stay tuned for our next deep dive into AI-powered text generation models! 🚀

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Final Thoughts: Why Start with Markov Chains?

💡 Simplicity: Easy to understand, no need for deep learning.
💡 Quick to implement: Requires only basic Python and text processing.
💡 Foundation for more advanced models: Helps grasp text patterns before moving to AI-driven techniques.

By mastering this, you’re taking the first step toward building powerful AI-generated text models! 🎯

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What’s Next?

In our next blog post, we’ll explore text generation techniques in more depth:

🔹 Comparing different models (Markov Chains vs. Neural Networks).
🔹 Understanding how GPT and transformers work.
🔹 Choosing the right approach for different applications.

👉 [Read the next post!]

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What do you think?

Would you like to see a side-by-side comparison of different text generation techniques? Let us know in the comments! 🚀