Sentence Predictor using Markov Chains
About Markov Chains
A Markov Chain is a mathematical system that undergoes transitions from one state to another according to certain probabilistic rules. In the context of text, each word (or character) is a state, and the probability of the next word depends only on the current word, not the sequence before it. This property is called the Markov property.
Project Overview
This project demonstrates the use of Markov Chains to generate sentences that mimic the style of a given text. The core of the project is the markov.py script, which reads a text file, builds a Markov model of word transitions, and then generates new sentences based on this model.
Structure of markov.py
- Text Parsing: Reads and tokenizes the input text.
- Model Building: Constructs a dictionary mapping each word to possible next words and their frequencies.
- Sentence Generation: Randomly selects a starting word and generates a sentence by following the Markov transitions.
This approach can be used for fun text generation, creative writing aids, or as an introduction to probabilistic models in natural language processing.