How ML and NLP Make Paraphrasing Tools Better

How ML and NLP Make Paraphrasing Tools Better

Paraphrasing tools have been around for a long time, and they’re pretty useful too. They can help in various types of writing tasks, and they can be a great tool for improving content quality.

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However, nowadays, there are some new and emerging technologies that are playing a role in improving the working and outputs of paraphrasing tools. Two of those technologies are Machine Learning (ML) and NLP (Natural Language Processing).

Now, NLP is pretty much vital to paraphrasing tools because they can’t function without it. Nevertheless, saying how NLP “makes them better” won’t be wrong since they do technically…make them better.

Let’s take a look at what NLP and ML are and how they play a role in improving the working of paraphrasing tools.

What are NLP and ML?

NLP and ML are two technologies that stand for Natural Language Processing and Machine Learning, respectively.

Natural Language Processing is essentially the technology that enables computer systems to understand natural languages, i.e., the ones that humans speak. To understand better, it has to be noted that computers usually don’t communicate in normal languages that humans speak. They communicate in binary.

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You won’t have one computer saying “Bonjour” to the other. They’d say something like, “01000010 01101111 01101110 01101010 01101111 01110101 01110010 00001010.”

In order to work with the information provided to them in natural languages, computers use NLP so that they can understand it.

Moving on, Machine Learning is the field that deals with equipping computer systems to learn from their inputs to improve the quality of their outputs. Machine learning essentially allows computer systems to perpetually learn and improve themselves.

Now that we’re done with the basic definitions, let’s move on to look at how they improve paraphrasing tools.

How ML and NLP Improve Paraphrasing Tools?

1. Role of NLP

NLP is, as we said earlier, more “vital” to paraphrasing tools than an “improver.”

NLP is necessary for paraphrasing tools since it allows them to understand the content that they are provided, which then allows them to make coherent changes to it. Even basic paraphrasing tools that don’t feature a lot of AI technologies/models use NLP to understand the given content.

The changes, be it synonymizing or something more extensive like sentence structure shuffling, are then made according to the meaning and context.

To show you an example of an actual paraphrasing tool, we’ve used a random one on the internet to rephrase sentence:

A lot of basic paraphrasing tools merely replace some of the words in the provided content with their synonyms, regardless of whether they are contextually suitable or not.

These types of tools don’t utilize AI, and the changes they make are somewhat mechanical. The alterations made by such tools are wholly based on the understanding of the plain meaning of the content rather than the contextual meaning. Even this is achieved with the help of NLP.

By the way, here is an example of a basic tool that we’ve described above:

2. Role of ML

Moving on, ML plays a role in improving paraphrasing tools as it helps them learn from the data they are provided. A paraphrasing tool that uses ML will be able to provide more and more improved results as it learns from user inputs.

Once a tool learns different words, phrases, styles, and semantics, it will be able to make smarter changes to the content, and it will be less likely to make contextual errors.

You can see one particular ML-equipped paraphrasing tool in the screenshot below:

Other than user inputs, ML-based paraphrasers can also be programmed to learn from certain resources on the internet. This can also help them improve over time and get better at providing smart results.


In this article, we looked at how NLP and ML can play a role in improving paraphrasing tools. It has to be remembered that there are a lot of other technologies and models that are used in paraphrasing tools, and they all work in conjunction to contribute to the overall work.

With the right ML algorithms in place, a paraphrasing tool will be able to improve itself constantly and provide coherent results against the provided content.