Introduction

The Plagiarism Sentence Detector project represents a cutting-edge application developed to identify instances of plagiarism within a set of sentences, leveraging the latest advancements in Natural Language Processing (NLP) and machine learning. This project, designed as part of an NLP class, showcases the practical application of computational linguistics to address real-world challenges in maintaining the integrity of written content.

Project Overview

Written in Python 3.8+, the Plagiarism Sentence Detector employs a sophisticated array of libraries including Gensim, sklearn, SciPy, and NumPy, in addition to utilizing pre-trained word vectors from Google News ('GoogleNews-vectors-negative300.bin') to analyze textual similarity. The core of the project lies in its ability to compare a target sentence, potentially plagiarized, against a set of original sentences, determining which of the originals the target most closely resembles in semantic content.

Core Functionality

Model Initialization (setModel): This function initializes the application by setting a pre-trained model as a global variable. This model, loaded with word vectors from Google News, is crucial for analyzing the semantic similarity between words and phrases within sentences.

Plagiarism Detection (findPlagiarism): The main function takes a list of original sentences and a target sentence as input. It then processes these sentences to identify which original sentence the target most likely plagiarized from, based on semantic similarity. This involves converting sentences into vectors using the model's word vectors, cleaning and preprocessing the sentences, and calculating cosine similarity between the vector representations.

Inner Mechanics

Text Preprocessing: Sentences are cleaned and normalized, stripping special characters and converting to lowercase, ensuring consistent analysis.
Vectorization: Sentences are converted into vectors by averaging the vectors of their constituent words, as provided by the pre-trained model. This process captures the overall semantic direction of each sentence.
Similarity Calculation: The cosine similarity between the target sentence's vector and each original sentence's vector is calculated, identifying the highest similarity score as the likely source of plagiarism.

Application and Impact

This project not only demonstrates the student's mastery of NLP and machine learning techniques but also addresses a pressing issue in educational and professional settings: plagiarism detection. By automating the comparison of textual content for semantic similarity, the Plagiarism Sentence Detector offers a powerful tool for educators, publishers, and content creators to uphold the originality and integrity of written work.

Conclusion

The Plagiarism Sentence Detector project stands as a testament to the application of complex algorithms and data structures to solve real-world problems, highlighting the developer's skill in programming, data science, and NLP. This sophisticated tool, capable of discerning subtle instances of plagiarism, exemplifies the potential of computer science to contribute meaningfully to various sectors, underscoring the developer's readiness for a career in this dynamic field.