English proficiency prediction NLP
Description :
IAS module project at ENIB (SP9 - 2021)
Basically the idea of the project is to predict the someone's English proficiency based on a text input.
We used the The NICT JLE Corpus available here : https://alaginrc.nict.go.jp/nict_jle/index_E.html
The source of the corpus data is the transcripts of the audio-recorded speech samples of 1,281 participants (1.2 million words, 300 hours in total) of English oral proficiency interview test. Each participant got a SST (Standard Speaking Test) score between 1 (low proficiency) and 9 (high proficiency) based on this test.
Tasks :
- Pre-process the dataset: extract the participant transcript (all
<B><B/>tags). Inside participant transcript, you can remove all other tags and extract only English words. - Process the dataset: extract features with the Bag of Word (BoW) technique
- Train a classifier to predict the SST score
- Compute the accuracy of your system (the number of participant classified correctly) and plot the confusion matrix.
- Try to improve your system (for example you can try to use GloVe instead of BoW).
Supervisor :
Olivier Augereau
Authors :
CORREA, Elias
GASSIBE, Franco