Text Generation: Using Markov Model & LSTM Networks to Generate Realistic Text

Abstract

Abstract: Text generation plays a crucial role in various natural language processing applications, ranging from creative writing to chatbots. This research delves into the realm of text generation by exploring and comparing two distinct techniques: Markov models and Long Short-Term Memory (LSTM) networks. The study focuses on their ability to generate realistic text within specific styles or genres, providing valuable insights into their respective strengths and limitations. Markov models, rooted in probability theory, and LSTM networks, a type of recurrent neural network, represent contrasting approaches to text generation. The research employs these techniques on a carefully curated dataset, evaluating their performance based on coherence, style, and contextual relevance. The comparison aims to elucidate the nuanced differences in how these models capture dependencies within the data and their effectiveness in simulating authentic linguistic patterns. Through rigorous experimentation, this research investigates the intricacies of both Markov models and LSTM networks, shedding light on their individual contributions to the task of text generation. The examination extends beyond mere algorithmic efficacy, considering the impact of these techniques on the quality and diversity of the generated text. Additionally, the study explores the influence of hyperparameters, such as temperature in the context of LSTM networks, on the output's richness and variability.