DarijaDistance is a specialized Python library crafted to handle the unique linguistic nuances of Moroccan Darija. It offers powerful tools for word comparison, distance measurement, translation lookup, and more; making it a valuable tool for natural language processing (NLP) tasks involving Darija.

• Word Distance Calculation: Calculate the distance between two words based on various factors, including letter differences, vowel swaps, and more.
• Closest Word Finder: Identify the closest words to a given word using positional encoding and other techniques.
• Translation Lookup: Retrieve potential translations for Darija words, including confidence scores.
• Customizable: Includes various methods to handle different types of word comparisons, including specific cases like vowel swaps and character replacements.

You can install DarijaDistance via pip:

pip install DarijaDistance

Alternatively, you can clone the repository and install it locally:

git clone https://github.com/aissam-out/DarijaDistance.git
cd DarijaDistance
pip install .

Here are some basic usage examples to get you started:

from DarijaDistance.word_distance import WordDistance

wd = WordDistance()

vowel_dist = wd.distance_between("kelb", "kalb")
consonant_dist = wd.distance_between("kelb", "kedb")
print(f"Vowel: {vowel_dist} - Consonant: {consonant_dist}")
# Vowel: 2.2 - Consonant: 4

repeated_letter = wd.distance_between("abc", "abbc")
different_letter = wd.distance_between("abc", "abdc")
print(f"Repeated: {repeated_letter} - Different: {different_letter}")
# Repeated: 2 - Different: 3

dist_1 = wd.distance_between("9alam", "qalam")
dist_2 = wd.distance_between("9alam", "3alam")
print(f"Distance A: {dist_1} - Distance B: {dist_2}")
# Distance A: 2.2 - Distance B: 4

distance = wd.distance_between("so9", "sou9")
print(f"Distance: {distance}")
# Distance: 0

Traditional distance measures like Levenshtein focus on the number of insertions, deletions, and substitutions required to transform one word into another. While useful, these methods treat all characters equally, ignoring the phonetic and linguistic nuances present in languages like Darija.

closest_words, min_distance = wd.get_closests("kulb")
print(f"Closest words to 'kulb': {closest_words} - min distance = {min_distance}")
# output: ['klb', 'kelb', 'kalb'] - 2.1

The WordDistance library encodes each character on a conceptual 3-dimensional plane, assigning numeric values to vowels, consonants and digits based on their relative importance and proximity within the Darija language. Summing these values creates a "sum image" for each word, simplifying comparisons and boosting performance. While this sum abstracts away some details, like the exact order of letters, it effectively reduces search complexity. Therefore, the integration of this 3-dimensional representation with summation produces a robust and efficient distance metric, positioning WordDistance as a superior tool for accurately assessing word similarities while ensuring optimal computational efficiency.

translation = wd.lookup_translation_word("klb")
print(f"Potential translations for 'klb': {translation}")

For tasks such as finding the closest words and looking up translations, the WordDistance library relies on the Darija Open Dataset (DODa), which was used to create the embedded pickle files that power these features. Specifically, the hash_table_word.pickle and hash_table_sum.pickle files. This structure enables efficient word lookup and comparison within the Darija language. However, for functions like distance_between() and other tools that do not require an underlying dataset, the library remains dataset agnostic and can operate with any words, regardless of their language or context.

wd.check_name("aissam")
# output: (True, {'potential translations': ['Aissam'], 'confidence': '100%'})
wd.check_name("tomobil")
# output: (False, {})

DarijaDistance library also includes a check_name method, designed to verify whether a given word is recognized as a name within the system. This function quickly scans the list of known names and returns a boolean indicating whether the word is a match. If a match is found, the function also provides potential translations along with a confidence score, ensuring that you can identify and work with names accurately in your applications.

The DarijaDataManager class provides easy-to-use methods for adding names and translations to your local datasets, ensuring your data is always up-to-date.

You can add a new name to the list using the add_name method. If the name already exists, it won't be added again.

from DarijaDistance.preprocess import DarijaDataManager

data_manager = DarijaDataManager()

data_manager.add_name("aissam")

Similarly, you can add new word translations using the add_translations method. The method ensures that only unique translations are added.

from DarijaDistance.preprocess import DarijaDataManager

data_manager = DarijaDataManager()

translations = [("la", "no"), ("klb", "dog")]
data_manager.add_translations(translations)

Contributions are welcome! If you have any ideas, suggestions, or find a bug, please open an issue or submit a pull request to the Github repo.

python -m unittest discover -s tests

This project is licensed under the MIT License. See the LICENSE file for more details.

If you have any questions or feedback, you can find me on LinkedIn: Aissam Outchakoucht or on X: @aissam_out.