scholarly journals Online Assignment Plagiarism Detector

Author(s):  
Nikhil Paymode ◽  
Rahul Yadav ◽  
Sudarshan Vichare ◽  
Suvarna Bhoir

Plagiarism is a big intricacy for companies, Schools, Colleges, and those who published their document on the web. In-Schools and Colleges maximum students write their assignments and experiments by copying other documents. Using this system teachers and examiners can detect the documents and sheets either it is written by a respective student or it is copied from someone else. For checking plagiarism the system takes two or more documents as a input and after using string matching algorithms, NLP ( natural language processing) technique, as well as an NLTK toolkit (natural language toolkit), produces output. In the output, the system returns some score which is an interval of 0 to 1. Where 1 and 0 refer to exactly similar and nothing is similar (Unique) respectively. If a score between 0 to 1 then it shows only some part of the document is similar. The main objective of the system is to find the more accurate plagiarism content in the documents with similar meanings and concepts that are correctly identified in an efficient manner. It is very easy to copy the data from different sources which includes the internet, papers, books over the internet, newspapers, etc. there is a need of detecting plagiarism to increase and improve the learning of students. To solve this problem, a student program plagiarism detection approach is proposed based on Natural Language Processing.

Designs ◽  
2021 ◽  
Vol 5 (3) ◽  
pp. 42
Author(s):  
Eric Lazarski ◽  
Mahmood Al-Khassaweneh ◽  
Cynthia Howard

In recent years, disinformation and “fake news” have been spreading throughout the internet at rates never seen before. This has created the need for fact-checking organizations, groups that seek out claims and comment on their veracity, to spawn worldwide to stem the tide of misinformation. However, even with the many human-powered fact-checking organizations that are currently in operation, disinformation continues to run rampant throughout the Web, and the existing organizations are unable to keep up. This paper discusses in detail recent advances in computer science to use natural language processing to automate fact checking. It follows the entire process of automated fact checking using natural language processing, from detecting claims to fact checking to outputting results. In summary, automated fact checking works well in some cases, though generalized fact checking still needs improvement prior to widespread use.


Author(s):  
Fredrik Johansson ◽  
Lisa Kaati ◽  
Magnus Sahlgren

The ability to disseminate information instantaneously over vast geographical regions makes the Internet a key facilitator in the radicalisation process and preparations for terrorist attacks. This can be both an asset and a challenge for security agencies. One of the main challenges for security agencies is the sheer amount of information available on the Internet. It is impossible for human analysts to read through everything that is written online. In this chapter we will discuss the possibility of detecting violent extremism by identifying signs of warning behaviours in written text – what we call linguistic markers – using computers, or more specifically, natural language processing.


2021 ◽  
Author(s):  
Viktoria Koscinski ◽  
Celeste Gambardella ◽  
Estey Gerstner ◽  
Mark Zappavigna ◽  
Jennifer Cassetti ◽  
...  

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