machine learning algorithms

Learn how the synergy of AI and Machine Learning algorithms in paraphrasing tools is redefining communication through intelligent algorithms that enhance language expression.

Artificial intelligence or AI as it is commonly called is a vast field of study that deals with empowering computers to be “Intelligent”.  This intelligence can manifest in different ways, but typically, it results in the automation of mundane tasks. However, the advancements in AI have led to automation in more sophisticated tasks as well. 

One of the most common applications of AI in a sophisticated task is text processing and manipulation. Which is also our topic today. Specifically, the paraphrasing of text with the help of AI. The most revolutionary technology that enables this is called machine learning. 

Machine learning is a subset of AI. So, when you say AI, it automatically includes machine learning as well. Now, we will take a look at how machine learning works in Paraphrasing tools. 

Role of machine learning algorithms in paraphrasing tools 

Machine learning by itself is also a vast field. There are a lot of ways in which a computer can process and manipulate text with machine learning algorithms.

You must have heard the name GPT if you are interested in text processing. GPT is one of the most popular machine-learning models used for text processing.  It belongs to a class of models called “Transformers” which are classified among deep learning models. 

And that was just one model. Transformers are the most popular when it comes to text processing and programmers have a lot of options to choose from. Many paraphrase generators nowadays utilize transformers in their back end for changing the given text. 

Most paraphrasing tools that are powered by AI are developed using Python because Python has a lot of prebuilt libraries for NLP (natural language processing).  

NLP is yet another application of machine learning algorithms. It allows computer systems to parse and understand text much in the same way a human would. So, let’s take a look at how a paraphrase generator works with these NLP libraries. We will check out a few different libraries and as such different transformers that are used nowadays for paraphrasing text.  

1. Pegasus Transformer

This is a part of the Transformers library available in Python 3. You can download Pegasus using pip with simple instructions. Machine learning algorithms will transform our lives, from autonomous vehicles to personalized medicine.

Pegasus was originally created for summarizing, however, the good thing about machine learning is that models can be tuned to do different things. So even though Pegasus is for summarizing, it can still be used for paraphrasing. 

Here’s how it works for paraphrasing. 

The transformer is trained on a large database of text, such a database is called a “corpus”. This corpus contains sentence pairs and each pair includes an original sentence and its paraphrased version. By training on such a corpus, the transformer learns how different sentences mean the same thing. Then it can create new paraphrases of any given sentence, even the ones it did not train on.  

2. T5 Transformer

T5 or text-to-text transfer transformer is a neural network architecture that can do a lot of things: 

• Summarizing 
• Translating 
• Question and answering 
• And of course, paraphrasing 

A paraphrasing tool that uses the T5 transformer can give a variety of different results because it is trained on a massive amount of data.  According to Google (the creators of T5), the T5 transformer was trained on Wikipedia, books, articles, and plenty of online web pages.  

T5 uses unsupervised learning which means it’s not told what is what, and it is allowed to draw its own conclusions. While that gives it extreme flexibility, it also gives more room for making errors. That’s why always proofread any text you get from a paraphrasing tool as it could have mistakes. 

3. Parrot Library

This particular library is not a transformer, but it uses similar techniques. It uses the same type of sequence-to-sequence architecture that is used in the T5 transformer.  

Another similarity between the two is that Parrot is also trained on a corpus of sentence pairs where one sentence is original and the other is paraphrased. This allows it to find patterns and realize that different syntax can still have the same meaning. 

Parrot uses a mix of supervised and unsupervised learning techniques. However, what sets Parrot apart from other models of paraphrasing is that it has two steps.  

Step one creates a bunch of paraphrases for the given text. However, it does not finalize them right away.  

Step 2 ranks the generated paraphrases and only selects the most highly ranked output. It uses a variety of factors to calculate rank and it is widely touted as one of the most accurate and fluent paraphrasing models available. 

Conclusion 

So, now you know something about how machine learning algorithms work in paraphrasing tools. These models are running on the server side of these tools, so the end user cannot see what is happening. 

The tool forwards the input to the models, and they generate an output which is shown to the user. And that is the simplest description of paraphrasing with machine learning. 

In this blog, we will discuss the top 8 Machine Learning algorithms that will help you to receive and analyze input data to predict output values within an acceptable range

1. Linear Regression 

Linear regression is a simple machine learning model and chances are you are already aware of it! Do you remember plotting the line y=mx+c in your introductory algebra class? This is an equation of a straight line where m is its gradient and c is the point where the line crosses the y-axis. Using this equation, you’re able to estimate the value of y for any given value of x. Similarly, linear regression involves estimating the relationship between independent variables (x) and a dependent variable(y).  

2. Logistic Regression 

Just like linear regression, logistic regression is a machine learning model used to determine the relationship between a dependent variable and one or more independent variables. However, this model is used for classification analysis. This is because logistic regression predicts the probability of an event occurring. For a probability greater than 0.5, a value of 1 is assigned, and for less than that 0. For example, you can use logistic regression to predict whether a student will pass (1) an exam, or they will fail (0). 

3. Decision Trees 

Decision tree is a supervised machine learning model that repeatedly splits the data based on a question corresponding to the features. The model learns the best way to reduce randomness and drafts a decision tree that can be used to predict the category of an item based on answering a selection of questions. For example, in the case of whether it will rain today or not, the questions can be whether it is sunny, did it rain yesterday, whether it is windy, and so on.  

4. Random Forest 

Random Forest is a machine learning algorithm that works similarly to a decision tree. The difference is that random forest uses multiple decision trees to make a prediction and hence decreases overfitting. The process of majority voting is carried out and the class selected by most trees is assigned to an item. For example, if two trees predict it to be 0, and one tree predicts it to be 1, then the class of 0 will be assigned to the item.  

5. K-Nearest Neighbor 

K-Nearest Neighbor is another simple machine learning algorithm that classifies new cases based on the category/class of the data points nearest to the new data point. That is, if most neighbors of an unknown item belong to class 1, then we assign class 1 to this unknown item. The number of neighbors to take into consideration is the value K assigned. If k=10, we will look at the 10 nearest neighbors of this item. The nearest neighbors are determined by measuring the distance using distance measures such as Euclidean distance, and the nearest are those that have the shortest distance. 

6. Support Vector Machine 

Support vector machines by dividing the data points using a hyperplane which is a straight line. The points donated by the blue diamond form one class on the left side of the plane and the points donated by the green circle represent another class on the right side of the plane. If we want to predict the class of a new point, we can simply determine it by whether it lies on the left or right side of the hyperplane and where it is within the margin. 

7. K-Means clustering 

K-means clustering is an unsupervised machine learning algorithm. That means it is used to work with data points whose class is not already known. We can use the clustering algorithm to group similar items into clusters. The number of clusters is determined by the value of K assigned. For example, you assign K=3. Three clusters are selected at random, and we adjust them until they are highly distinct from one another. Distinct clusters will have points similar to each other but these points will be distinct from points in another cluster.

8. Naïve Bayes

Naïve Bayes is a probabilistic machine learning model based on the Bayes theorem that assumes that all the features are independent of one another. Conditional probability refers to the probability of an outcome occurring if it is given that another event has occurred. This algorithm predicts the probability that an item belongs to a particular class and is assigned the class with the highest probability. 

Share more Machine Learning algorithms with us

Have we missed any Machine Learning algorithm that you would like to learn about? Share with us in the comments below