Book Image

Hands-On Markov Models with Python

By : Ankur Ankan, Abinash Panda
Book Image

Hands-On Markov Models with Python

By: Ankur Ankan, Abinash Panda

Overview of this book

Hidden Markov Model (HMM) is a statistical model based on the Markov chain concept. Hands-On Markov Models with Python helps you get to grips with HMMs and different inference algorithms by working on real-world problems. The hands-on examples explored in the book help you simplify the process flow in machine learning by using Markov model concepts, thereby making it accessible to everyone. Once you’ve covered the basic concepts of Markov chains, you’ll get insights into Markov processes, models, and types with the help of practical examples. After grasping these fundamentals, you’ll move on to learning about the different algorithms used in inferences and applying them in state and parameter inference. In addition to this, you’ll explore the Bayesian approach of inference and learn how to apply it in HMMs. In further chapters, you’ll discover how to use HMMs in time series analysis and natural language processing (NLP) using Python. You’ll also learn to apply HMM to image processing using 2D-HMM to segment images. Finally, you’ll understand how to apply HMM for reinforcement learning (RL) with the help of Q-Learning, and use this technique for single-stock and multi-stock algorithmic trading. By the end of this book, you will have grasped how to build your own Markov and hidden Markov models on complex datasets in order to apply them to projects.
Table of Contents (11 chapters)

Part-of-speech tagging

The first problem that we will look into is known as part-of-speech tagging (POS tagging). According to Wikipedia, POS tagging, also known as grammatical tagging or word-category disambiguation, is the process of marking up a word in a text as corresponding to a particular part of speech based on both its definition and its context, that is, its relationship with adjacent and related words in a phrase, sentence, or paragraph. A simpler version of this, which is usually taught in schools, is classifying words as noun, verbs, adjectives, and so on.

POS tagging is not as easy as it sounds because the same word can take different parts of speech in different contexts. A simple example of this is the word dogs. The word dogs is usually considered a noun, but in the following sentence, it acts like a verb:

The sailor dogs the hatch.

Correct grammatical tagging...