Exploring N-Gram Based Text Generation: A Probabilistic Approach

Introduction

Text generation is a fundamental task in Natural Language Processing (NLP) that has various applications, from autocomplete systems to chatbot responses. One of the simplest yet effective approaches to text generation is based on n-grams, a probabilistic model that predicts the next word given a sequence of previous words.

In this study, we implemented bigram-based text generation using probability distributions derived from a given corpus. While this method does not capture deep semantics, it provides a structured way to generate syntactically coherent text.

Understanding N-Gram Models

An n-gram is a contiguous sequence of n words from a given text corpus. The probability of a word occurring is dependent on the previous (n-1) words, following the Markov Assumption: $$

$P(w_n | w_{n-1}, w_{n-2}, ..., w_1) \approx P(w_n | w_{n-1})$ 

This simplification allows us to estimate probabilities efficiently.

We implemented a bigram model (n=2), which computes the probability of a word occurring given the previous word. The model was trained on a limited vocabulary dataset, which affects its ability to generalize but serves as a foundation for structured text generation.

Implementation Overview

1. Tokenization: The input text was split into individual words.
2. Bigram Construction: Pairs of consecutive words were extracted.
3. Probability Estimation:
   • Count previous word occurrences.
   • Count bigram occurrences.
   • Compute conditional probabilities using: $P(w_n | w_{n-1}) = \frac{C(w_{n-1}, w_n)}{C(w_{n-1})}$
4. Sentence Generation:
   • Start with a seed word.
   • Predict the next word based on probabilities.
   • Repeat until a stopping condition is met.

Observations and Limitations

The generated sentences maintained a basic grammatical structure but lacked deeper coherence due to the absence of long-range dependencies and semantic understanding. Common limitations include:

• Lack of Vocabulary Generalization: Unseen words result in failure to generate meaningful sequences.
• Short-Term Memory: The model only considers one previous word.
• No Semantic Awareness: Words are selected based on frequency rather than meaning.

Next Steps: Addressing Semantic Awareness

While n-gram models provide a structured approach to text generation, they are inherently limited in capturing meaning. Moving forward, we aim to incorporate semantic understanding using:

• Word Embeddings (Word2Vec, GloVe, FastText) to capture word relationships.
• Neural Language Models (LSTMs, Transformers) for contextual awareness.
• Smoothing Techniques (e.g., Laplace Smoothing) to handle unseen words.

By integrating these approaches, we aim to enhance the quality of generated text and move towards more intelligent and coherent language models.

Conclusion

This work marks an important step in probabilistic text generation. The n-gram approach provides a simple yet effective method for structured text generation, forming the foundation for more advanced techniques. As we progress, we will refine our models to capture not only syntactic correctness but also deeper semantic understanding, making text generation more meaningful and contextually aware.