Natural Language Processing (PART-2) Probability Models Introduction: Markov Models for Text.

Author(s): AbhinayaPinreddy

Originally published on Towards AI.

1. Overview

Probability models are crucial in understanding how uncertain events evolve over time, especially in machine learning and AI. Among these models, Markov Models play a foundational role in sequence modeling, reinforcement learning, computational biology, and text processing.

The article explains Markov models, their applications in reinforcement learning, hidden Markov models, and their importance in text generation. It highlights key concepts like the Markov property, joint distribution, and provides examples of Markov models in practical applications. The piece emphasizes training techniques, smoothing methods, and offers insights into probabilistic text generation, culminating in a discussion on the usability and limitations of Markov models in various contexts.

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Published via Towards AI