TOPIC: Automatic Detection of Plagiarism using Natural Language
Processing
ABSTRACT:
Plagiarism detection of papers selected for conference
publications and journals, or of assignments handed over for
evaluation is very important; it ensures the work submitted is free
from any copied content. Plagiarism detection tools that are
currently available suffer from certain drawbacks: these tools are
unable to detect plagiarism if the grammatical construct of the
sentence is changed or if the words used in a sentence are replaced
by their synonyms. This project attempts to improve the efficacy of
plagiarism detection tools by incorporating aforementioned changes
into them and ensuring that these tools are not fooled by such
changes made in the semantics of the paper by its authors. It
proposes a framework for plagiarism detection using a number of NLP
techniques that are applied to the document that not only analyses
the sentences but also its structure and semantics. Results are
obtained with respect to the websites that can be accessed freely,
and a corpus of published works as available on www.arvix.org
REFERENCES:
1. Using Natural Language Processing for Automatic Detection of
Plagiarism Research group in Computational Linguistics, Stanford
Universityhttp://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.458.9440&rep=rep1&type=pdf
2. Using Natural Language Parsers in Plagiarism Detection ISCA
Archivehttp://isca-speech.org/archive_open/archive_papers/slate_2007/sle7_077.pdf
3. Architecture of the Semantically Enhanced Intellectual
Property Protection System Springer
Publicationshttp://link.springer.com/chapter/10.1007/978-3-319-00969-8_70
GROUP MEMBERS:
1. Takshak Desai (60004120019)2. Udit Deshmukh (60004120020)3.
Mihir Gandhi (60004120031)
TOPIC: Adaptive Test Taking Software using Machine Learning
ABSTRACT:
Computerised adaptive tests are a form of computer based tests
that adapt to the examinees ability level. These tests are
typically designed by universities for entrance examinations so
that they can simultaneously test the ability and skill of the
student. This project attempts to design a generic software for
adaptive test taking in which the user can feed in his/her
questions according to the level of difficulty. The software then
makes use of Machine Learning algorithms such as Nave Bayesian
Classifier to decide the questions asked to the student, depending
on the questions he/she has answered. The number of correct
questions answered and the level of difficulty of the questions
answered will decide the final score of the candidate.
REFERENCES:
1. A web-based tool for Adaptive Testing International Journal
of Artificial Intelligence in
Educationhttp://iaiedsoc.org/pub/954/file/954_Conejo04.pdf
2. Procedures for selecting items for Computerized Adaptive
Tests Applied Measurement in
Educationhttp://www.tandfonline.com/doi/abs/10.1207/s15324818ame0204_6
3. Computerized Adaptive Testing A Primer by H. Wainer
GROUP MEMBERS:
1. Takshak Desai (60004120019)2. Udit Deshmukh (60004120020)3.
Mihir Gandhi (60004120031)
