# short-answer-grader

**Repository Path**: wangchong0217/short-answer-grader

## Basic Information

- **Project Name**: short-answer-grader
- **Description**: An automatic short answer grader.
- **Primary Language**: Python
- **License**: Not specified
- **Default Branch**: master
- **Homepage**: None
- **GVP Project**: No

## Statistics

- **Stars**: 0
- **Forks**: 0
- **Created**: 2019-05-12
- **Last Updated**: 2020-12-19

## Categories & Tags

**Categories**: Uncategorized

**Tags**: None

## README

# A Short Answer Grader

This is an automatic short answer grading system written in Python. Given a question, a correct reference answer and a student response, it computes a real-valued score for the student response based on its semantic similarity with the correct answer. This is the grader reported in the NAACL 2016 short paper **Fast and Easy Short Answer Grading with High Accuracy** (except the term weighting-based features). The directory contains a pretrained grader (pretrainedGrader.py) as well as source code for training a new one (trainAndApplyGrader.py). Each source file contains an actual example of grading a student response.

## Requirements

1) NLTK  
2) Scikit-learn  
3) The [Python wrapper for Stanford CoreNLP](https://github.com/dasmith/stanford-corenlp-python)

## Installation and Usage
1) Make sure the requirements are installed.  
2) Change the statement "rel, left, right = map(lambda x: remove_id(x), split_entry)" in **corenlp.py** to "rel, left, right = split_entry".  
3) Download the grader:  

	  git clone https://github.com/ma-sultan/short-answer-grader
4) Download the word embeddings by Baroni, Dinu, and Kruszewski from [this link](http://clic.cimec.unitn.it/composes/semantic-vectors.html) (the "Best predict vectors") and put the file in the Resources directory.  
5) Run the **corenlp.py** script to launch the server:  

	  python corenlp.py
6) To run the pretrained grader, run **pretrainedGrader.py**.  
7) To train a new grader and apply to a test item, run **trainAndApplyGrader.py**.