multilang-probe 0.1.6

A Python package for analyzing multilingual text.

multilang-probe

A Python package for analyzing multilingual text.

Overview

multilang-probe is a toolkit designed to classify character sets, detect languages in text files, and extract specific multilingual passages. It supports character detection for a wide range of writing systems using Unicode script properties (e.g., Latin, Japanese, Cyrillic, Arabic, Devanagari, and more). Additionally, it leverages the FastText model for robust language detection.

Whether you are analyzing large corpora or extracting specific language data, multilang-probe simplifies the process with an easy-to-use API.

Features

Character Set Classification:

• Detect and calculate proportions of character types (e.g., Latin, Japanese, Cyrillic, Arabic, Devanagari) in text.
• Uses regex with Unicode script properties (\p{Script}) for more accurate classification.
• Special handling for Japanese vs Chinese characters (Han script).

Example: Character Detection

from charlang_detect.character_detection import classify_text_with_proportions

text = "これは日本語と English です。"
proportions = classify_text_with_proportions(text)
print(proportions)
# Possible output:
# {"japanese": 50.0, "latin": 50.0}

Explanation:

• If the text contains Hiragana/Katakana, Han characters are considered Japanese Kanji.
• Otherwise, Han characters are considered Chinese.

Language Detection:

• Identify top languages in text using Facebook's FastText pre-trained model.

Example: Language Detection

from charlang_detect.language_detection import detect_language_fasttext

text = "Ceci est un texte en français."
languages = detect_language_fasttext(text)
print(languages)
# Output example: "fr: 99.2%, en: 0.8%"

Corpus Analysis:

• Analyze all .txt files in a folder to detect multilingual passages and language distributions.
• Character-based filtering: Identify and filter text lines containing specific character sets (e.g., Japanese, Cyrillic, Arabic).
• Language-based filtering: Extract passages in a specific language, with customizable confidence thresholds (e.g., 70%).
• Targeted extraction: Extract lines of text meeting both minimum length requirements and language detection accuracy.
• Calculate language proportions: Aggregate detected languages across files and calculate their proportions.

Example: Analyze and Detect Multilingual Passages

from charlang_detect.corpus_analysis import analyze_corpus_with_fasttext

folder_path = "path/to/corpus/"
results = analyze_corpus_with_fasttext(folder_path)
for filename, langs in results.items():
    print(filename, langs)

Example: Filter Passages by Character Types

from charlang_detect.corpus_analysis import filter_passages_by_character_types

folder_path = "path/to/corpus/"
character_types = ["japanese", "cyrillic"]
filtered = filter_passages_by_character_types(folder_path, character_types)
for filename, passages in filtered.items():
    print(filename, passages)

Example: Extract Passages by Language with Threshold

from charlang_detect.corpus_analysis import filter_passages_by_language

folder_path = "path/to/corpus/"
target_languages = ["fr", "en"]
threshold = 70
filtered = filter_passages_by_language(results, target_languages, folder_path, threshold)
for filename, passages in filtered.items():
    print(filename, passages)

Supported Character Sets

• Japanese (Hiragana, Katakana)
• Han (Kanji; considered Japanese if Hiragana/Katakana present, else Chinese)
• Korean (Hangul)
• Cyrillic (for languages like Russian, Bulgarian, etc.)
• Arabic
• Hebrew
• Greek
• Latin (basic and extended)
• Devanagari (e.g., Hindi, Sanskrit)
• Tamil, Bengali, Thai
• Extendable via Unicode scripts
• "other" category for characters not belonging to known scripts

Dependencies

• Python 3.7+
• FastText
• Regex (for Unicode script classification)

License

This project is licensed under the MIT License. While the MIT License allows unrestricted use, modification, and distribution of this software, I kindly request that proper credit be given when this project is used in academic, research, or published work. For citation purposes, please refer to the following:

CAFIERO Florian, 'multilang-probe', 2024, [https://github.com/floriancafiero/multilang-probe].

Contributing

Pull requests are welcome! For major changes, please open an issue first to discuss what you would like to change.

Author

Florian Cafiero
GitHub: floriancafiero
Email: [e-mail]

Future Features

• Support for other pre-trained language models (e.g., spaCy).
• Visualization tools for multilingual analysis.
• CLI (Command-Line Interface) for easy usage without writing code.