ESL Feature Extraction (EFE)

This project explores Natural Language Processing (NLP) techniques for feature extraction in ESL/TOEFL specifically Error Identification questions. By identifying these features, the project aims to improve the accuracy of competency classification.

Getting Started

• Prerequisites:
  • Python 3.10.13
  • Spacy
  • en_core_web_sm (You can use the md or lg model if you like)
  • TOEFL ITP Error Identification type questions (csv format)
• Installation: Use virtual env to install dependencies from the requirements.txt

Running the Code

• Basic Usage: Run the main.py followed by -f or --filename to specify the filename of the error identification question data.
• Examples: python main.py -f <filename>
  • <filename>: should be the filename of the Error Identification question data on data/<filename>.csv path
  • -f --filename: Flag for input filename in data/ folder with .csv format
• Output: <filename>_features_v2.json located in data/ folder

Notes

• This project is actively exploring Natural Language Processing (NLP) techniques for feature extraction in ESL/TOEFL questions, with a focus on Error Identification. By identifying these features, the project aims to improve the accuracy of competency classification in ESL/TOEFL assessments. Additionally, it serves as a learning platform for delving deeper into the capabilities of NLP.