Hidden Markov Model in Machine Learning and Its Applications


The appearance of AI has changed critical thinking and direction by utilizing information-driven approaches. Among the plenty of AI strategies, the Secret Markov Model (Gee) stands apart as a strong and refined probabilistic model. Gee has tracked down the boundless application in different spaces, including discourse acknowledgment, bioinformatics, money, and then some.

This blog offers a far-reaching investigation of Stowed away Markov Models, diving into their center standards, reasonable applications in certifiable situations, and a bit-by-bit guide on carrying out them utilizing Python.

What is the Hidden Markov Model in Machine Learning?

Hidden Markov Model machine learning is a statistical model that is based on the principles of Markov chains. In a Markov chain, the future state of a system is dependent only on its current state, making it a memoryless process. HMM extends this concept by introducing hidden states that are not directly observable but generate observable outputs, also known as emissions.

The “hidden” aspect of HMMs refers to the fact that the underlying state is not directly accessible; instead, we observe the emissions that provide clues about the hidden state.

Hidden Markov Models are characterized by three key components:

Component Description Example (Weather Prediction)
States These are the hidden variables in the model that represent the underlying system states. “Rainy,” “Sunny,” “Cloudy”
Emissions These are the observable outputs generated by each state. In the weather example, the emissions could be the types of clothing people wear on a particular day. “Umbrella,” “Sunglasses,” “Jacket”
Transitions The transitions represent the probabilities of moving from one state to another. In the weather example, the transitions would indicate the probabilities of transitioning from a “sunny” to “cloudy” day or from a “cloudy” to “rainy” day. P(“Sunny” to “Cloudy”) = 0.4 P(“Cloudy” to “Rainy”) = 0.2

Hidden Markov Model With an Example

Let’s consider a classic example of weather prediction using HMM Machine Learning. Suppose we are interested in predicting the weather (rainy, sunny, cloudy) based on observable factors like the type of clothing people wear. The weather state is hidden, but we can observe people’s clothing choices. By analyzing the sequence of observed clothing choices over time, we can infer the hidden weather states using an HMM.

For instance, if people are frequently wearing sunglasses and light clothing, the HMM might infer that the weather state is “sunny.” Conversely, if people are carrying umbrellas and wearing jackets, the model might infer a “rainy” weather state. Learn more about HMM via Master of Science in Machine Learning & AI from LJMU. 

Application of Hidden Markov Model

Hidden Markov Model Python finds applications in a wide range of fields due to their ability to model sequential data. Some notable applications include:

  • Speech Recognition: HMMs are used to convert speech signals into text. By modeling phonemes as hidden states and audio features as emissions, HMMs can accurately recognize spoken words.
  • Bioinformatics: In gene prediction, HMMs can identify genes in DNA sequences by modeling exons and introns as hidden states and nucleotides as emissions.
  • Finance: HMMs are employed to model financial time series data, such as stock prices, to predict market trends and make informed investment decisions.
  • Gesture Recognition: HMMs are utilized to recognize and interpret human gestures from video sequences, enabling applications like sign language interpretation.

Hidden Markov Models in NLP

Natural Language Processing (NLP) is another domain where HMMs have found widespread use. One of the essential NLP tasks is Part-of-Speech (PoS) tagging, where each word in a sentence is assigned a grammatical label. HMMs have been successfully employed for PoS tagging due to their ability to model sequential data effectively.

In PoS tagging, the words in a sentence are treated as the observable emissions, and the PoS tags are considered as the hidden states. By learning the probabilities of transitions between PoS tags and the probabilities of emitting words given a particular PoS tag, HMMs can accurately tag words in unseen sentences. Gain in-depth knowledge about HMM in ML through Executive PG Program in Machine Learning & AI from IIITB. 

Limitations of Hidden Markov Models

While Hidden Markov Models are versatile and powerful, they do have certain limitations:

  1. Limited Memory: HMMs have a finite memory and can only capture dependencies within a fixed window of states. This limitation might impact their ability to model long-term dependencies in sequential data.
  2. Assumption of Stationarity: HMMs assume that the underlying distribution of states and emissions remains constant over time, which might not hold in some real-world scenarios where the distribution changes over time.
  3. Inability to Handle Long Sequences: As the sequence length grows, the complexity of HMMs increases significantly, leading to computational challenges. This makes them less suitable for modeling very long sequences of data.

Implementation of HMM using Python

Now, let’s dive into the practical aspect of implementing Hidden Markov Models using Python. Python provides various libraries that simplify HMM implementation, such as hmmlearn and pomegranate. We’ll walk through a step-by-step guide to building an HMM for a simple weather prediction problem.

Here are the steps involved in implementing the HMM using Python:

  • Install the Required Libraries: Before we start, make sure you have the necessary libraries installed, such as numpy, hmmlearn, and matplotlib.
  • Data Preparation: Prepare the data for training the HMM. In the weather prediction example, you might have a dataset that contains observed clothing choices and corresponding weather states.
  • Model Training: Use the data to train the HMM. The hmmlearn library provides classes for building and training HMMs.
  • Model Evaluation: After training, evaluate the performance of the HMM on a separate test dataset. You can use metrics such as accuracy and confusion matrix.
  • Making Predictions: Once the HMM is trained and evaluated, you can use it to make predictions on new sequences of observed emissions

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What is PoS-tagging?

Part-of-Speech (PoS) tagging is a fundamental task in Natural Language Processing (NLP) that involves assigning grammatical tags to each word in a sentence. These tags represent the syntactic category or part of speech that the word belongs to, such as noun, verb, adjective, adverb, pronoun, preposition, etc.

Sure! Here’s the information presented in a table format:

Step Description Example (Sentence: “The quick brown fox jumps over the lazy dog”)
Tokenization Initially, the sentence is divided into individual words or tokens. This step ensures that each word is treated separately for PoS tagging. “The,” “quick,” “brown,” “fox,” “jumps,” “over,” “the,” “lazy,” “dog”
Tag Assignment Subsequently, each word in the sentence is assigned a PoS tag based on its context and linguistic characteristics. “The” (determiner), “quick” (adjective), “brown” (adjective), “fox” (noun), “jumps” (verb), “over” (preposition), “the” (determiner), “lazy” (adjective), “dog” (noun)
Tagset PoS tags are drawn from a predefined tagset, which comprises a set of categories representing different parts of speech. PoS Tags: Determiner, Adjective, Noun, Verb, Preposition, etc. (from a standard PoS tagset)

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PoS Tagging with Hidden Markov Model

Part-of-Speech (PoS) tagging is a critical task in Natural Language Processing (NLP) that involves assigning grammatical tags to each word in a sentence. Hidden Markov Models inmachine learning offer an effective approach to tackle this problem by learning the underlying patterns and dependencies between words and PoS tags in a given corpus.

The PoS tagging process with HMMs can be summarized as follows:

  • Training Data Preparation: To train an HMM for PoS tagging, a labeled corpus is required, where each sentence is annotated with its corresponding PoS tags. The corpus should include a variety of sentences to cover different linguistic patterns and ensure the model’s generalization.
  • Building the HMM: The next step is to construct the HMM using the labeled corpus. The HMM consists of hidden states representing PoS tags and observable emissions representing words in the sentences. The model aims to learn the probability distribution of transitioning between hidden states (PoS tags) and the probability distribution of emitting observable emissions (words) from each hidden state.
  • Learning Transition Probabilities: During training, the HMM analyzes the labeled corpus to estimate the probabilities of transitioning from one PoS tag to another. For example, it learns how likely it is to transition from a noun to a verb or from an adjective to a noun based on the observed corpus.
  • Learning Emission Probabilities: The HMM also learns the probabilities of emitting specific words from each PoS tag. It calculates how likely it is for a particular PoS tag to produce certain words in the training corpus.
  • Viterbi Algorithm for Tagging: Once the HMM is trained, it can be used to perform PoS tagging on new, unseen sentences. The Viterbi algorithm is commonly employed to find the most likely sequence of hidden states (PoS tags) given the observed sequence of words. This algorithm efficiently computes the best PoS tag sequence by considering both transition and emission probabilities.
  • Tagging Unseen Sentences: With the HMM trained and the Viterbi algorithm in place, the model can accurately predict the PoS tags for words in unseen sentences. It assigns the most probable PoS tags to each word based on the learned probabilities from the training corpus.
  • Evaluation and Refinement: After completing the PoS tagging process, the model’s performance is assessed using various metrics like accuracy, precision, recall, and F1 score. If the obtained results are not deemed satisfactory, the model can be improved through adjustments to hyperparameters or the inclusion of additional training data.

Let’s take a look at how we can calculate these two probabilities for a set of sentences:

  • Mary Jane can see will 
  • The spot will see Mary
  • Will Jane spot Mary?
  • Mary will pat Spot

The below table is a counting tableau for the words with their part of speech type

Words Noun Modal Verb
mary 4 0 0
jane 2 0 0
will 1 3 0
spot 2 0 1
can 0 1 0
see 0 0 2
pat 0 0 1

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Implementation in Python

We’ll use the nltk library in Python to implement the PoS tagging HMM. The nltk library provides several pre-tagged corpora, making it convenient for training the HMM.

Here are the steps involved in implementing PoS tagging using HMM in Python:

  • Data Preparation: Obtain a corpus with tagged sentences. The nltk library provides corpora like the Brown Corpus, which is annotated with PoS tags.
  • Model Training: Use the tagged corpus to train the HMM. The HMM will learn the probabilities of transitions between PoS tags and the probabilities of emitting words given a particular PoS tag.
  • PoS Tagging: After training the HMM, you can use it to tag words in unseen sentences. The model will assign the most likely PoS tags to each word.

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Hidden Markov Models play a vital role in machine learning, offering a powerful way to model sequential data and make predictions based on observations. They find applications in various domains, from speech recognition to NLP, making them a valuable tool in the AI and ML toolkit.

By understanding the concepts and implementation of HMMs, we can unlock their potential to solve complex problems and gain valuable insights from sequential data. Acquire deeper understanding of HMM in ML via Executive PG Program in Data Science & Machine Learning from university of Maryland. 


Are hidden Markov models considered machine learning?

Hidden Markov Models are a class of machine learning algorithms used for modeling sequential data and making predictions based on observed emissions.

What are the applications of Hidden Markov Models in machine learning?

Hidden Markov Models are widely used in speech recognition, bioinformatics, finance, gesture recognition, and Natural Language Processing tasks like PoS tagging.

Can you explain the difference between a Hidden Markov Model and a regular Markov Model in machine learning?

The main difference lies in the observability of states. In a regular Markov Model, all states are directly observable, whereas, in a Hidden Markov Model, some states are hidden and generate observable emissions.

What are some real-world examples of using Hidden Markov Models for data analysis and prediction?

Real-world examples include predicting stock market trends, identifying genes in DNA sequences, speech-to-text conversion, and gesture recognition in human-computer interaction.

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