How we at insideAIML uses NLP to check Plagarism?

Neha Kumawat

a year ago

Today, let I explain to you how InsideAIML teamwork behind and try to handle plagiarism using Natural Language Processing (NLP) techniques so that our user should always get unique and new content.
As we know NLP is a very important link between human and machine. It helps humans to interact with the computers and at the same time when techniques of artificial intelligence such as machine learning and deep learning are combined with it, produces an excellent result. For example, Siri, Alexa, and chatbots.
NLP Plagiarism Checker
NLP Plagiarism Checker
Here, we use NLP algorithms to check plagiarism. Now, you might be thinking how does this algorithm work in order to put a check on plagiarism? 
So, lets me explain you in a very simple and a straightforward it is done by parsing or breaking down sentences into small bits or tokens and then processing the same in pieces. It uses a very popular method which is known as ‘Latent Semantic Analysis’ or ‘LSA.’

How LSA helps us in checking Plagiarism?

LSA has a very scientific approach towards NLP based plagiarism checking. In other words, it really helps us to analyze up to what extent two words are similarly based on cosine similarity values of the vectors being reproduced by the words that are taken for comparison. 
The proximity of these values helps us to come up with a conclusion about the similarity between the words. The process may sound pretty straightforward, but in reality, the application of NLP in plagiarism checking involves a lot of mathematical and statistical calculations involving ‘Lexical Analysis, Syntactic Analysis,’ and even a much-refined approach of the algorithm with particular emphasis on grammar. 

Some of the other Algorithms of NLP are

We also, use some of the advanced techniques such as BERT. Some of the researchers defined it as
“BERT stands for Bidirectional Encoder Representations from the Transformers. It is designed to pre-trained deep bidirectional representations from the unlabeled text by jointly conditioning on both left and right contexts. As a result, the pre-trained BERT model can be fine-tuned with just one additional output layer to create state-of-art models for a wide range of NLP tasks.”
Here, I am not going to explain to you the intuition behind the BERT algorithm. For this article, it is out of scope. But I will really try to write a separate article on it.
Apart from some of these algorithms, there are also some other algorithms present in NLP, such as ‘MinHash or Locality-sensitive Hashing, SimHash and Text Profile Signature’ that use even better scientific techniques as compared to LSA for checking plagiarism. 
However, the overall idea behind using any NLP techniques is all about breaking down the sentences into small pieces and checking sentences first with the words, and then finally, the main idea gets portrayed in the matter. 
The plagiarism check based on NLP may also act as a refinement tool for the content as this process removes stop-words or words that are burdening the data without adding any value in a sentence. 
So, in a way, we try to apply NLP techniques which plays a pivotal role in the field of plagiarism checking and protection of intellectual property rights and always try to provide our users a better and unique content.
I hope you enjoyed this article and get to how InsideAIML team uses NLP techniques to check plagiarism.
For more blogs/courses on data science, machine learning, artificial intelligence and new technologies do visit us atInsideAIML.
Thanks for reading…

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