https://github.com/grantgasser/plagiarism-detection-nlp
Detecting plagiarism with a focus on feature engineering
https://github.com/grantgasser/plagiarism-detection-nlp
containment feature-engineering plagiarism plagiarism-detector sagemaker
Last synced: 7 months ago
JSON representation
Detecting plagiarism with a focus on feature engineering
- Host: GitHub
- URL: https://github.com/grantgasser/plagiarism-detection-nlp
- Owner: grantgasser
- Created: 2019-07-16T03:28:59.000Z (about 6 years ago)
- Default Branch: master
- Last Pushed: 2019-07-22T20:09:44.000Z (about 6 years ago)
- Last Synced: 2025-03-18T17:57:24.269Z (7 months ago)
- Topics: containment, feature-engineering, plagiarism, plagiarism-detector, sagemaker
- Language: Jupyter Notebook
- Homepage:
- Size: 146 KB
- Stars: 17
- Watchers: 2
- Forks: 7
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
Awesome Lists containing this project
README
# Plagiarism Project, Machine Learning Deployment
This repository contains code and associated files for deploying a plagiarism detector using AWS SageMaker.
## Project Overview
In this project, you will be tasked with building a plagiarism detector that examines a text file and performs binary classification; labeling that file as either *plagiarized* or *not*, depending on how similar that text file is to a provided source text. Detecting plagiarism is an active area of research; the task is non-trivial and the differences between paraphrased answers and original work are often not so obvious.
This project will be broken down into three main notebooks:
**Notebook 1: Data Exploration**
* Load in the corpus of plagiarism text data.
* Explore the existing data features and the data distribution.
* This first notebook is **not** required in your final project submission.
**Notebook 2: Feature Engineering**
* Clean and pre-process the text data.
* Define features for comparing the similarity of an answer text and a source text, and extract similarity features.
* Select "good" features, by analyzing the correlations between different features.
* Create train/test `.csv` files that hold the relevant features and class labels for train/test data points.
**Notebook 3: Train and Deploy Your Model in SageMaker**
* Upload your train/test feature data to S3.
* Define a binary classification model and a training script.
* Train your model and deploy it using SageMaker.
* Evaluate your deployed classifier.
---
Please see the [README](https://github.com/udacity/ML_SageMaker_Studies/tree/master/README.md) in the root directory for instructions on setting up a SageMaker notebook and downloading the project files (as well as the other notebooks).