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How We at InsideAIML Uses NLP to Check Plagarism?
Neha Kumawat
2 years ago
Table of Contents
- Introduction
- How LSA helps us in checking Plagiarism?
- Some of the other Algorithms of NLP
Introduction
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.
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
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…