Sentence rephrasing has always helped make your communication with other people smooth and effective. It is basically a process where you alter a sentence’s original wording and structure and deliver it while keeping its meaning the same.
Whether you are writing a book or an academic essay, a blog, a product description, or even retelling a story to your friend that your grandmother told you the other night, sentence rephrasing finds its application in almost every type of writing.
Yes. Sentence rephrasing can be performed manually. How hard it would be to replace the word “good” with “fine” in a sentence? Due to technological breakthroughs these days, various rephrasing tools have been using NLP (Natural Language Processing) and ML (Machine Learning) algorithms to help you rephrase a sentence.
In this article, we are going to understand how these two help you rephrase a sentence. We’ll dive deep into the details of NLP and ML paradigms and see how they rephrase a sentence.
What Does Sentence Rephrasing Even Mean?
Before we dive in and discuss the NLP and ML paradigms and see how they operate on sentence to alter their structure, let’s understand what sentence rephrasing even mean.
Sentence rephrasing is changing the wording and structure of a sentence to produce its alternate version which has the same meaning. The rephrasing technique is very simple. It involves the following three major parts:
- Replacement of words with relevant synonyms
- Changing the structure of the sentence
- Switching between passive and active voice
The main goal of rephrasing a sentence is to keep the meaning as it is. There are many reasons why people use it. For example, if you are writing a blog and have to borrow an idea from another person’s published content, you cannot write it in your blog as it is.Because it will be plagiarized.
To avoid this from happening, you rephrase the idea in your own words and write it in your blog. In this way, you can easily borrow the idea from another person and make your content more authentic.
Another reason why people rephrase sentences is to change their deliverance tone. Since you replace the words with different synonyms, you can modify the sentence to adapt to whatever nature you require.
Now that we have comprehensively understood what rephrasing a sentence means and why people use it, let’s get on to discuss the real business which is understanding the paradigms, or process, of how NLP and ML works in rephrasing a sentence.
Let’s discuss it one by one.
NLP Paradigms for Sentence Rephrasing:
NLP, or Natural Language Processing, is a field of Artificial Intelligence that helps machines understand and process the natural human language. You see, machines do not operate in natural language (the words we human beings speak.) NLP helps them to understand and interpret whatever information we feed them.
So, to rephrase a sentence, a tool has to understand the provided information before it can make the required information. To understand this information, these tools leverage NLP algorithms.
To understand how it happens, study the points below.
1. Preprocessing of Text:
The sentence rephrasing process begins here. The tool receives your provided information or text and applies preprocessing protocols to it.
The basic protocols that take place in this step include tokenization and lemmatization. The tool breaks down the sentence into different parts, or tokens, in the tokenization phase. For example, a sentence saying, “I like eating apples,” will be broken into parts like:
“I” “Like” “Eating” “Apples”
In the lemmatization protocol, however, the words of a sentence are broken down to their root form for more simplification. For example, the words ‘providing’ and ‘provided’ will be lemmatized into ‘provide’.
These preprocessing protocols make it easier for the sentence rephraser to learn and process the text further.
2. Semantic Evaluation:
The next step is the semantic evaluation of the text. Here the machine learns the contextual meaning of the provided information. This part is crucial because it determines the quality of the output rephrased sentence.
The computer uses the word embedding to evaluate the contextual meanings. It either uses the vector representations of the words by using methods like GloVe or Word2Vec or employs more advanced methods like BERT (Bidirectional Encoder Representations from Transformers.)
This advanced technique helps generate contextual word embedding. This means that this technique evaluates the word contextually and by studying the surrounding words of that specific one. For example, in the sentence “John plays guitar,” BERT will provide synonyms for “play” in the sense of playing music and not in the sense of playing a sport.
The usage of general or advanced word embedding depends on the tool you are using.
3. Syntax Analyzation:
Once the program has evaluated the contextual sense of the sentence, it performs its syntax analysis. Evaluating and changing the structure of a sentence is an important part of sentence rephrasing.
Syntax analysis helps the machine evaluate the grammatical structure of the sentence so that the machine/tool can alter it accordingly and accurately. Mostly parsing programs like Stanford Parser are used to evaluate the structural organization of sentences.
4. Application of Rephrasing Algorithm Strategies:
Once the tool has evaluated the contextual meaning and grammatical structure of the sentence, it determines what kind of rephrasing algorithm strategy has to be applied to get the required output.
This is the part where actual rephrasing happens.
In simple words, the tool determines what changes are to be made to the content. Commonly used rephrasing algorithm strategies can be:
- Rule-based
- Statistical
- Neural network-based
The tool/machine may use a combination of all of these strategies in one sentence if required.
5. Evaluating the Rephrased Sentence:
Before the tool gives you the rephrased sentence it has generated with the help of NLP, it evaluates its quality first.
This step is pretty easy to understand. The tool uses different protocols to see how finely the rephrased sentence matches the context and does it fulfill the human standard expectations or not.
The programs that may be employed to evaluate the rephrased sentence’s quality are BLEU (Bilingual Evaluation Understudy) and ROUGE (Recall-Oriented Understudy for Gisting Evaluation.)
That’s the whole procedure of how NLP programs process a sentence to provide you with its rephrased version.
Now, let’s discuss how ML does it.
ML Paradigms for Sentence Rephrasing:
ML, or Machine Learning, works a little differently from NLP in rephrasing a sentence. Where NLP understands the context of a sentence to rephrase it, ML uses a large dataset of sentences and their rephrased versions to predict how the required rephrased sentence will be formed.
Since there is a slight difference in the operating nature of these two programs, the process of rephrasing also varies a little bit.
Let’s break down the steps that are involved in this type of sentence rephrasing to understand the nature of ML paradigms in the rephrasing tools.
1. Feeding Large Dataset:
As we mentioned above, ML programs operate according to an already available data set. Therefore, the ML programs in the rephrasing tools are fed with a huge data set of two types of sentences.
These sentences include an original version and a rephrased version. The reason why these two types of sentences are stored in the tool is to help the tool learn both the differences and similarities between them. It also helps them learn what kind of linkage they have to create in these sentences to keep the original meaning of the alternate sentence the same as the original.
This data is used as the raw material that ML programs use to predict and generate rephrased sentences.
Here’s an important thing you have to keep in mind to understand this step. It’s not like you have to feed a huge dataset every time you rephrase a sentence with the help of ML. It doesn’t work like that.
The data storing job is done one time. The tool, then, uses this data to predict and provide rephrased sentences every time you use it.
2. Identifying Features:
The second step in this process is identifying features. But what does it even mean by feature?
Features of a sentence are its major and important parts that are, in one way or another, important to understand its nature. In this step, these features are extracted to help the machine learn and evaluate the sentence better.
There are a few programs that are used in this phase. The widely used techniques used by NLP to extract features from a sentence include:
- TF-IDF (Term Frequency-Inverse Document Frequency)
- Count Vectorization
The basic purpose of this extraction is to help the tool learn the nature of sentences better. Once the tool learns the features, or the important parts, of a sentence, it helps them evaluate where the changes are to be made.
3. Model Training:
Remember the dataset that was fed to the program to help make appropriate predictions? Well, it is used in this step.
Model training is simply making the tool learn what possible changes have to be made in the sentence that is about to be rephrased. But how do we do that? it’s simple. By allowing it to study the data we have provided it with already.
The tool studies and analyzes this data to understand and make the most accurate and sense-making changes.
A commonly used program to help the model train is TensorFlow. The APIs and other tools available in this tool help the tool understand the sentence and evaluate its rephrased version.
4. Predicting and Rephrasing:
Now that the tool has learned different models that can help it to make the right predictions by studying the data, the rephrasing part takes place.
The tool understands the commands you provide such as altering the tone e.g., creative, fluent, formal, etc., and then applies changes accordingly.
The rephrased sentence is generated by applying the rephrasing patterns and rules that the tool has learned in the models we discussed previously. While bringing in changes, the tool also keeps in mind the real meaning and idea of the original sentence.
The rephrased sentence has different wording and structure, yet it holds the same meaning as the original one.
5. Quality Assurance:
Once the tool has generated an alternate version of the original sentence, it checks its quality before deploying it. This process is performed to ensure that the rephrased sentence is according to the provided instructions of the users or not.
Programs that help the tool in the quality assurance process include the ones we have mentioned in the NLP section (ROUGE, BLEU.) Although there are some other programs also used by some tools to evaluate the quality of the rephrased sentence. These include:
- METEOR (Metric of Evaluation of Translation with Explicit Ordering)
- TER (Translation Edit Rate)
Once the alternate version of the original sentence passes the quality assurance test, it is provided to the user as the rephrased version of the original one.
This is how ML helps the tool to rephrase a sentence. The nature of this procedure can vary from tool to tool, but it is a basic route the rephrasing tools take to provide you with alternate versions of different sentences.
The procedure is also the same for the content consisting of multiple sentences.
Conclusion:
Rephrasing is an important part of almost every type of writing. It helps writers make their content unique and bring the required tone to their content. There are various steps included in the rephrasing technique which includes the replacement of words with synonyms and alteration of sentence structure.
Although the technique can be performed manually, there are many online rephrasing tools available that do this for you automatically. People are using these tools almost every day but not everyone knows how the ML and NLP processes in these tools to provide you a rephrased sentence.
In the information given above, however, we have provided comprehensive information on how these programs act as tools to rephrase a sentence.
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