Python's Role In Next-Gen Text Paraphrasing Tools

Paraphrasing is something we do a lot in our everyday lives. If you notice, you will find that we say and repeat things we have read or heard to others in our own words. It is rare for us to recount something exactly how we read or heard it.

Paraphrasing is a valuable skill, and it shows that the paraphraser (the person who paraphrases) has a great command of their language. Writers in particular find paraphrasing quite valuable. It helps them convey their message more effectively and improves the quality of their content.

Nowadays, anyone can effectively paraphrase any given text without even being fluent in the language. That is due to next-gen paraphrasing tools—they can paraphrase text automatically in a matter of seconds.

Today, our topic is to explore and see what the role of Python is —an object-oriented programming language— in the making and working of these next-gen paraphrasing tools.

The Role of Python in Next-Gen Paraphrasing Tools

In this section, we will check out why Python in particular is so good for developing paraphrasing tools. So, let’s begin.

1. Python is a Powerful Programming Language

Python is a high-level programming language. What does high-level mean? It simply means that its syntax is closer to that of natural human language thus it is easy to understand for humans.

A language that is easy to understand is also easy to use and manipulate. You can create highly advanced programs with Python simply because it is easy to use. Another thing that makes Python powerful is that it is object-oriented.

Object-oriented programming (OOP) languages provide design solutions that are extremely efficient at handling complex tasks. Any sophisticated software that is in use today is built using an OOP language.

Python is particularly powerful because it is easier to use than other OOP languages like C++ and Java. Hence, it is well suited to create complex AI-driven software like a paraphrasing tool.

2. Python has Powerful AI Libraries, Functions, and Models

Precisely because of the reasons we highlighted in the previous sections, Python has found widespread use in the field of artificial intelligence. Some libraries can deal with text processing, manipulation, and even generation. The same goes for images. You can process images and manipulate them in various ways with the help of libraries built in Python.

Since our topic is related to paraphrasing, which is basically text manipulation, we will limit our discussion to that. Natural language processing (NLP) Libraries like NLTK (Natural Language Tool Kit) provide important functions for parsing and understanding text for paraphrasing. They include:

• Tokenization—a process in which sentences are broken down into either words or letters. Each smallest division is called a token. Tokens are used for further processing of the text.

• Syntax analysis—a process in which token relations are checked to see if they are correct. This includes checking word spelling and sentence grammar.

• Semantic analysis—a process in which the meaning of the text is understood and checked to see whether it makes sense or not.

• Discourse integration—the process in which the context of the text is understood. The most important part of NLP, it helps the program to understand what each sentence means using context gained from the previous sentences.

Without these powerful functions, next-gen paraphrasing is impossible. Due to these NLP functions, paraphrasers made using Python are capable of accurate rewording and sentence altering without changing the meaning or the context.

3. Python has a Huge Community Support

If Python wasn’t already good enough, it has something that other languages seldom have, a huge community. Many communities of Python create open-source functions and libraries that can help with AI paraphrasing.

The biggest example of this is Hugging Face — “an AI community building the future”. There are so many open-source AI programs and functions you can find on this website that it is nigh impossible to not find what you need.

The best thing about a huge community is that many people can help you. If you ever get stuck somewhere, simply making a forum post is guaranteed to get you a plethora of correct answers.

With such a huge community advancing AI even further, Python is positioned perfectly for the creation and development of next-gen paraphrasing tools.

4. Can Scrape the Web for Datasets

NLP is a product of machine learning—an application of AI that has the property of learning from datasets and then being able to draw conclusions based on it. The most important thing here is the datasets themselves.

The better the data you have the better your model’s training will be. And wouldn’t you know it, Python is frequently used for web scraping. It has been used for sentiment analysis by scraping data from social media such as comments and posts.

Similarly, you can use Python to obtain huge datasets from websites such as Kaggle to train paraphrasing machine learning models.

This does not mean that other programming languages are unable to scrape data from the web. It’s only that Python makes it exceedingly simple to do so. When you pair everything Python has to offer, it becomes clear that Python is the only conceivable option for developing next-gen paraphrasers.

That’s why Python has such a big role in the development of such tools.

Examples of Paraphrasers Made Using Python

Now, we will look at some tools online paraphrasing tools that are available today and have been developed using Python. That should give you a good idea of the role of Python in paraphrasing tools.

1. Paraphrasing.io

This online paraphrasing tool was made using a mixture of HTML, CSS, and JavaScript for its front end, while the back end uses PHP and Python. PHP is simply there to store forms and login data and whatnot. The main ingredient of course is the server-side Python which enables the tool to paraphrase text in five different ways.

This freemium tool has two free modes and three premium modes. All of them perform admirably and maintain the meaning and context of the original text while changing its syntax.

2. Paraphraseonline.io

Next, we have another tool called Paraphrase Online. This tool is completely free and has six different modes. It also has Python working on the backend to paraphrase any inputted text using AI.

Speaking of AI there is even a dedicated AI mode. This mode can change the text so much (without altering the context) that it even increases the length of the text by a few percent. Here is an image that shows the text length increasing from 125 to 170.

The best thing about it is that the text is really interesting to read. It has enhanced our write-up and made it more readable as well as engaging. This is truly the work of a next-gen paraphraser.

3. Quillbot.com

This is another top-of-the-line paraphrasing tool that uses Python in the backend. This amazing paraphraser has a grand total of eight paraphrasing modes. You can also create a custom mode using the bits of functions from other modes.

It is one of the top-ranked tools available online. You can see for yourself its great results. Just look at the provided image. In it, we have used a mode called “Fluency” that improves the readability of the text. And we can see how it has done so.

So, that should tell you plenty about the role of Python in next-gen paraphrasing tools.

Conclusion

In this article, we have covered the role of Python in next-gen paraphrasing tools. We saw the advantages that Python provides over other programming languages. We saw how it has a huge community specifically dedicated to the development and refinement of AI systems. All of these things have resulted in Python being the top choice in the development of AI paraphrasers.

We finished our discussion by looking at three top-ranked AI paraphrasers that are known to use Python. Hopefully, after all of that, it is now clear that Python has a huge role to play in next-gen paraphrasing tools.