REPHRASING REVOLUTION: HOW PYTHON LIBRARIES FACILITATE TEXT TRANSFORMATION

Advancement in technology has completely revolutionized the technique of rephrasing. With the help of online rephrasing tools, generating alternate versions of text has become way easier than manual methods.

python libraries in text transforamtion — Python Libraries

These tools function on the basis of Natural Language Processing (NLP) algorithms. NLP takes the assistance of Python libraries to understand the human-written text and amend it. but how does this all actually work?

That’s what this blog is going to be about. Throughout this blog, we will try to find out how Python libraries facilitate text transformation.

What are Python Libraries?

In simple words, a Python library is a group of codes that programmers use to run different types of programs. They function as a collection of program-related modules that prevent the hast of writing the same codes again and again.

In text transformation, these libraries are used to analyze human-written text. After analysis, they help apply different protocols to the text to provide us with alternative versions.

How Python Libraries Facilitate Text Transformation?

Python library assist paraphrase tool in text transformation by providing necessary resources to bring changes in the text by using alternate words and phrases. Several Python libraries can be used for this purpose.

These libraries participate in a series of steps that make the rephrasing tool transform the provided text. Studying these steps will help us understand how the technique of text rephrasing has revolutionized in these times.

Below is the list of these steps that clearly demonstrate how Python libraries facilitate text transformation.

Breaking Down of Text

The first step that rephrasing tools perform when you provide them with your initial text is to break it down into parts. Why do they do it? It is simple to understand. Since computers cannot understand human language, breaking it down helps them to analyze it.

This process is also known as tokenization.

What actually happens is that the tool separates the text into different parts. These parts can be words, punctuation marks, sub-words, etc. For example, the sentence “I ate an apple.”, will be broken down like this:

“I”
“Ate”
“An”
“Apple”

Breaking down the text like this is helpful for applying the upcoming (in the blog) protocols to the text.

The Python libraries that are useful for this step include NLTK, SpaCy, Huggingface Transformers, etc. However, the choice of library totally depends on what type of changes you want in the text.

Part-Of-Speech Tagging

Once the text is broken down into parts, Python libraries identify the part of speech. This means that the libraries determine the noun, verb, adjective, etc., of the text. This technique helps the rephrasing tools to maintain the grammatical correctness of the rephrased text.

In this step, different parts of a text are labeled with their grammatical role.

Here’s how the Python libraries will assist the rephrasing tool in labeling the parts of text grammatically according to the above-given example:

I (Pronoun)
Ate (Verb)
An (Determiner)
Apple (Noun)

Tagging these parts of speech helps keep the rephrased text grammatically correct.

Dependency Parsing

Once tagged with grammatical roles, the words are analyzed to see how they will be connected to each other. This thing is known as dependency parsing. Connecting these words with each other is crucial for the contextual preservation of a rephrased text.

It’s all ML and Python libraries in action here. It is not possible for a computer to predict the connection of words in binary language. Python libraries assist it in understanding and connecting words with each other for better context.

Rephrasing of Text

Once all of the above-mentioned steps are completed, the actual rephrasing of the text takes place in the tool. Whatever we discussed before was a tool preparing to rephrase a text with the assistance of Python libraries.

Now, these libraries are going to help the tool generate alternate versions of the original text.

It can happen in two ways depending on the type of tool we are using.

Rule-Based Rephrasing:

The clue’s in the word itself. It is a type of rephrasing where you set some rules (or give the computer some commands) and the tool rephrases the given text accordingly. These rules can be about the replacement of synonyms or sentence restructuring. Here’s an example to understand it better.

If the original sentence says, “The weather is very hot”, the rule-based rephrased sentence can be, “The temperature is extremely high.”

Machine Learning-Based Rephrasing:

This method employs Machine Learning algorithms to generate a rephrased text. No template or rules are given to the tool here. Instead, the tool learns from the previous patterns and fed data to make predictions about how a text would be rephrased.

Different transformer models are used for this purpose. GPT-3 is a common example of it. Here’s how the above example text will be rephrased in ML-based rephrasing.

“The weather is very hot,” to “It’s scorching outside.”

The Review Process:

Once the Python libraries have assisted the rephrasing tool to generate alternate versions of a text, the review process begins. You can also call it the proofreading process. Here, the tool checks the rephrased text for possible errors.

These steps help ensure the quality of the rephrased text. There are a few metrics that can be utilized in the review process if you have provided reference data sets to the tool.

These metrics include BLEU, ROGUE, etc.

Finalization:

Now it’s all done. You are about to get your rephrased text. After all of these steps, the tool displays you with rephrased version of a given text. In the finalization process, the tool provides the user with the generated rephrased text.

The rephrased version can be single or multiple. That totally depends on what type of tool you are using. Some tools even provide you with various synonyms for a single word to choose from. That is only to ensure a good user experience.

If the user is not satisfied with the rephrased text, they can either generate it again or make manual changes by choosing different options given within the tool. This can include selecting the rephrasing modes or changing synonyms etc.

Conclusion:

Natural Language Processing algorithms have revolutionized the technique of rephrasing a text. Many rephrasing tools utilize Python libraries to get assistance in text rephrasing. These libraries help the computer understand the human written text and perform commanded actions.

In the information that we have provided above, you can find a detailed note on how these Python libraries facilitate text rephrasing in many tools these days. It’s a process consisting of different steps that make the text rephrasing quick and efficient.