This repository provides an implementation of text generation based on Markov chains. Markov chains are mathematical models that predict the next state in a sequence based solely on the current state, making them suitable for tasks like text generation.
Overview Markov chains are used to model systems that transition from one state to another. In the context of text generation, a Markov chain can be utilized to generate sequences of text that mimic the style of an input text by leveraging statistical patterns.
Character-Level Markov Chain In a character-level Markov chain, the model predicts the next character based on a fixed-length sequence of preceding characters. The process involves:
State Definition: Defining states as sequences of a specific length (e.g., pairs or triplets of characters). Transition Probabilities: Calculating the probability of each character following a given state by analyzing the frequency of occurrences in the input text. Text Generation: Generating new text by starting with a random or predefined state and using the transition probabilities to predict and append the next character iteratively. Word-Level Markov Chain In a word-level Markov chain, the model operates on sequences of words rather than individual characters. The process involves:
State Definition: Defining states as sequences of a specific number of words. Transition Probabilities: Calculating the likelihood of each word following a given sequence of words based on their frequencies in the input text. Text Generation: Generating new text by starting with a random or predefined state and using the transition probabilities to predict and append the next word iteratively. Implementation Details The implementation is provided in Python and consists of:
markov_chain.py: Contains the core implementation of the Markov chain models for both character-level and word-level text generation. It includes:
MarkovChainTextGenerator: A class for generating text based on character-level Markov chains. MarkovChainWordGenerator: A class for generating text based on word-level Markov chains. example_usage.py: Demonstrates how to use the provided classes to generate text.
Character-Level Markov Chain The MarkovChainTextGenerator class constructs a transition matrix from the input text, where each state is a sequence of characters, and the transitions are the subsequent characters. The model generates text by predicting the next character based on the current state.
Word-Level Markov Chain The MarkovChainWordGenerator class constructs a transition matrix from the input text, where each state is a sequence of words, and the transitions are the subsequent words. The model generates text by predicting the next word based on the current state.