text-language-identifier accurately identifies English, French and Italian written text with up to 99% accuracy.

Since this project was used to better understand n-gram analysis, no natural language processing modules were imported -- everything was implemented from first principles.

0. Download this repo as a .zip
1. cd src
2. $ python3 lang-bigram-id.py

Accuracy information for each model will be displayed in the terminal after the analysis is complete.

Diff the output files to see which lines were predicted differently by certain pairs of models. Here are some commands to try:

$ diff ../output/letter-bigram-laplace-smoothing-predictions.txt ../output/letter-bigram-no-smoothing-predictions.txt
$ diff ../output/letter-bigram-laplace-smoothing-predictions.txt ../output/word-bigram-no-smoothing-predictions.txt
$ diff ../output/letter-bigram-laplace-smoothing-predictions.txt ../output/word-bigram-laplace-smoothing-predictions.txt

This program creates a probabilistic model of each language based on bigram analyses of French, English and Italian sample corpora. To predict the language of a test sentence, it creates another probabilistic model to represent the sentence and chooses the language whose model is most similar to the sentence model using the RMSE.

This is was a computational linguistics experiment to see which language model, word or letter bigrams, performs the best. I also tested the impact of LaPlace smoothing on the predictive accuracy of the model.