detectLanguage

Purpose

The purpose of this repository is twofold:

Create train/test samples of sentences and their relevant languages from Tatoeba for NLP-related tasks.
Evaluate the performance of solutions meant to detect the language of a given piece of text to identify a reliable solution (spoiler: langid).

Tatoeba

Tatoeba is a database of sentences and their translations. Currently, there are +9 million sentences and +400 supported languages. The content is created and maintained by a community of volunteers. The data is freely available under a Creative Commons Attribution (CC-BY) license:

Tatoeba -- https://tatoeba.org/ -- CC-BY License

Tatoeba content has been used in other projects including the Tatoeba Translation Challenge maintained by the Language Technology Research Group at the University of Helsinki and the Tatoeba Tools Python library maintained by L.Beaudoux.

Components

Currently, there are three main components in this repository.

create_Tatoeba_train_test.py

A script for generating customizable train-test samples of sentences and their languages. Such samples are useful for other NLP-related tasks (e.g. evaluating machine translations).

The default languages are English, Chinese, German, Spanish, French, Italian, Japanese, Korean, Portuguese, Danish, Dutch, and Norwegian. Any subset can be specified using the --languages flag.

The default threshold of sentences per language is 5,000. If a language has fewer sentences than the threshold available in the corpus then it won't be included in the output. Any integer can be specified using the --min_sentences flag.

The --sample_type flag gives the option to specify a simple random sample or a random sample stratified by sentence word/character length, the default is random.

You can generate unique sets of train/test samples using --number_sets, the default is 1. The train/test split is a standard 80/20.

usage: create_Tatoeba_train_test.py [-h] [--languages [LANGUAGES ...]] [--minimum_sentences MINIMUM_SENTENCES] [--sample_type {random,stratify}] [--number_sets NUMBER_SETS]

optional arguments:
  -h, --help            show this help message and exit

  --languages [LANGUAGES ...]
                        languages to include in output

  --minimum_sentences MINIMUM_SENTENCES
                        minimum number of sentences found in corpus

  --sample_type {random,stratify}
                        type of sample to take: "random" or "stratify"

  --number_sets NUMBER_SETS
                        number of train-test sets to generate

evaluate.ipynb

A notebook for evaluating the performance of solutions for detecting the language of a given text. It's currently focused on langid and langdetect. I prefer langid due to its speed and better performance identifying Chinese, although both solutions achieve similar F1 scores.

Includes F1 scores, confusion matrices, and compiling the results as a function of sentence length to facilitate plotting.

plot_results.ipynb

A notebook for plotting the performance results by language and as a function of sentence length.

get_predictions.ipynb

A notebook for generating predictions for a large number of samples and finding the average F1 scores by language and by sentence length. It takes several hours (+8) to run and the results for 100 samples are not drastically different than the results for 1 sample.

Bcromas/detectLanguage