Understanding the Role and Use of Python in Paraphrasing Tools
Paraphrasing tools are AI-based tools. They mostly exist as online tools and are a great boon for writers everywhere. Writers of all skill levels use them for a variety of purposes. Some of them use them for getting better ideas, others use them to deal with duplication, and some of them use them for learning.
One of the most popular languages used in the development of paraphrasers is Python. Python is a powerful, general-purpose programming language that is used in a variety of fields. Its applications are vast, as it is used in developing apps, web apps, and scripts.
In this article, we will take a look at the Python language, and its relation with rephrasing tools.
Python as a Programming Language
Python is a high-level programming language that can utilize both a procedural paradigm and object-oriented programming (OOP). It is also a language that does not require a compiler as it is an interpreted language. This means that instead of a compiler, there is an interpreter, and they differ because the interpreter analyzes the code at runtime, unlike a compiler which analyzes the code before running it.
It is a high-level language, meaning that it is close to natural language, as such, it is easier for the programmer to understand. It is interpreted by the system into a lower language level so that it can be understood by the central processing unit (CPU).
It is very flexible because it is a general-purpose language. You can use it to develop any sort of application. It is widely used for automating mundane and repetitive tasks in the workplace. However, in this article, our focus is on its use for AI applications, particularly the paraphrasing tools.
Role of Python in Paraphrasing Tools
In a rephrasing tool, AI is one of the most important ingredients. With Python that is not too difficult to do.
NLP in Python and Paraphrasing Tools
Python is used for NLP (natural language processing) which is an application of AI. NLP is the most important thing in a paraphrasing tool as it allows it to understand the text and then alter it in a suitable way.
Let’s check out how NLP works and how it relates to a paraphrasing tool. Given below are the steps involved in NLP.
Lexical Analysis
In any AI program that involves text, the first step is always lexical analysis. This involves breaking down the text into its basic components. That means an entire article will be divided into paragraphs, sentences, words, and finally characters (in that order).
It is also known as tokenization. The smallest division i.e., a character is called a token. Once the text is completely tokenized, then the process moves forward.
Syntax Analysis
In this phase of NLP, the program checks the relation between the tokens. It checks whether the tokens are correct or not grammatically. Obviously, a single token is not enough to do this checking process. It actually checks the individual words and complete sentences for grammar.
All the words are rearranged in different ways to create sentences to check if they make sense or not. The original version of the sentence is also checked for grammatical soundness.
Sentences that are not correct are rejected by the program. Their syntax is rearranged until it forms a proper sentence.
Semantic Analysis
In this phase, the program checks the meanings of the words and sentences and whether they make sense or not. This is necessary because during the syntax analysis, a sentence may be formed that is correct with respect to the syntax, but it could have no proper meaning i.e., a bag ate a broom. That sentence is nonsense because bags can’t eat anything, much less a broom.
These are the kinds of things that are checked in the semantic analysis and corrected. So far, all the steps were just for prepping the text. The next two steps of NLP are what makes them so useful for paraphrasing.
Discourse Integration
This is the main part of NLP which makes it so useful for paraphrasing. This is where the program is able to understand the context.
In discourse integration, the meaning of a given sentence is understood by the meaning of the sentence before them. Words like “It”, “they”, and “them” do not make sense until there is a previous context for them. Sometimes these words are explained in the same sentence that they were used in. Other times the context is created in the previous sentence.
So, the program basically checks how a sentence is understood in light of the prior sentence. This is very useful in paraphrasing because it allows an AI paraphraser to alter the text in much better ways. It allows a paraphraser to change multiple sentences while keeping their context intact for maximum syntactic changes.
Pragmatic Analysis
This is the final step in natural language processing. It involves analyzing word combinations as well as keeping track of what was said or done by whom as well as the recipient of those words and actions.
It uses the context and meaning gained from the previous steps to understand the true meaning of a phrase/sentence. For example, the phrase “keeled over” could mean a lot of things depending on the context. Literally, it means to fall over. But as a saying/proverb it could also mean “receiving a mental shock.”
This kind of derivation of the true meaning is done in the pragmatic analysis stage. A paraphrasing tool uses the results of the pragmatic analysis to use phrases and words that are contextually correct as replacements for the original words and phrases in the text.
The great thing about NLP in Python is that you don’t even need to program it yourself. There are numerous NLP libraries available for Python that allow you to harness it for any sort of application.
Python for Paraphrasing
In Python, there are many different libraries known as pre-trained Transformers that can manipulate text. They utilize NLP to understand it, and it is up to the programmer to make a paraphraser, a summarizer, or any other kind of text manipulator from it.
A few famous transformers are the Pegasus transformer and T5 transformers. One T5 transformer that is particularly popular is the “Parrot Paraphraser”. These are all free and open-source libraries, meaning that you can use them without paying for them.
You can find the Parrot paraphraser on GitHub and use it in your own programs. Programmers can pick it up, make their own adjustments and create a paraphrasing tool out of it.
And that is just one transformer. Advanced tools that have more options for paraphrasing basically use multiple transformers for different modes. They can use a simple model that only uses synonyms for creating a basic mode. Then they can start moving up the ladder and use more sophisticated transformers for creating advanced modes.
In this way Python can be used to create paraphrasers that can:
- Detect tones.
- Create plagiarism-free content.
- Use contextually correct synonym/phrase replacement.
- Provide accurate and fast outputs.
Python makes it all possible due to its flexibility and huge community support.
Conclusion
In this article, we checked out how Python is vital for paraphrasing tools. It is a general-purpose language that is very easy to pick up. It is also an interpreted language which makes it independent of any platform which makes it very flexible as well.
For those reasons, it is a great language for creating AI applications. There is a huge community surrounding Python, which means that there are a lot of open-source libraries for almost anything, including NLP libraries for paraphrasing. These libraries make Python the ideal tool for creating paraphrasers, which is compounded by the fact that many paraphrasing tools today are made using Python.