Mastering Multilingualism: Top 5 Python Language Detection Techniques Explained

Accurate language detection is a fundamental aspect of many NLP applications. In this comprehensive guide, we’ll delve into the top 5 language detection techniques available in Python. Each technique offers unique advantages and is well-suited for different scenarios. By the end of this guide, you’ll be equipped to choose the right technique for your specific use case.

Photo by Kelly Sikkema on Unsplash

Table of Contents:

1. Introduction
2. Technique 1: Polyglot
3. Technique 2: Langdetect
4. Technique 3: Gcld3
5. Technique 4: FastText
6. Technique 5: spaCy
7. Conclusion

Technique 1: Polyglot — Robust Multilingual Support

Overview: Polyglot is a versatile library that excels in detecting languages from various scripts. It supports over 100 languages, making it a valuable tool for multilingual applications. Polyglot is a Python library that provides various NLP functionalities, including language detection, named entity recognition, and more. It supports a wide range of languages and is particularly useful for multilingual text processing.

Key Features:

• Comprehensive multilingual NLP library.
• Supports language detection, named entity recognition, part-of-speech tagging, and more.
• Pre-trained models available for various languages.
• Designed for cross-lingual applications.

Before we begin, make sure you have its required dependencies installed. You can install it using pip:

!pip install polyglot
!pip install icu
!pip install pyicu
!pip install pycld2

from polyglot.detect import Detector

def detect_language_polyglot(text):
    try:
        detector = Detector(text)
        detected_language = detector.language.code
        confidence = detector.language.confidence
        return detected_language, confidence
    except Exception as e:
        print("An error occurred:", e)
        return None, 0.0

sample_text = "Hello, cómo estás?"
detected_language, confidence = detect_language_polyglot(sample_text)
print(f"Detected language: {detected_language} with confidence: {confidence:.2f}")

Pros:

• Offers a range of NLP functionalities beyond language detection.
• Supports many languages out of the box.
• Handles multilingual text processing effectively.

Cons:

• Requires additional installation of language-specific models for optimal performance.
• May not provide the same level of accuracy as specialized language detection tools.

Use Cases:

• Multilingual content analysis for social media monitoring.
• News aggregation platforms that process articles in various languages.

Conclusion: Polyglot is a versatile NLP library suitable for multilingual text analysis. While it excels in various NLP tasks, including language detection, it’s particularly beneficial when dealing with texts from different languages within a single application.

Technique 2: Langdetect — Simple and Effective

Overview: Langdetect is a lightweight library that provides language detection with minimal setup. It’s based on Google’s Language Detection API.

Key Features:

• Lightweight and fast language detection.
• Based on n-gram frequency analysis.
• Supports a wide range of languages.
• Designed for simplicity and ease of use.

You can install required dependencies it using pip:

!pip install langdetect

from langdetect import detect

def detect_language_langdetect(text):
    try:
        detected_language = detect(text)
        return detected_language
    except Exception as e:
        print("An error occurred:", e)
        return None

sample_text = "Bonjour, comment ça va?"
detected_language = detect_language_langdetect(sample_text)
print(f"Detected language: {detected_language}")

Pros:

• Straightforward implementation and usage.
• Supports a variety of languages.
• Quick language identification for short texts.

Cons:

• May not perform as accurately on very short or mixed-language texts.
• Limited to language detection and doesn’t provide additional NLP functionalities.

Use Cases:

• Email clients for automatically setting language preferences.
• Browser extensions for translating web content based on detected language.

Conclusion: Langdetect is a lightweight and easy-to-use library that provides quick and efficient language detection. While it may not be as accurate as more advanced techniques, it’s suitable for scenarios where simplicity and speed are essential.

Technique 3: Gcld3 — Fast Language Identification

Overview: Gcld3 (Google Compact Language Detector 3) is a language identification tool developed by Google that provides fast and reliable language detection. It’s designed to work well on short text snippets and offers excellent performance.

Key Features:

• Fast and lightweight language identification.
• Capable of handling short text inputs.
• Provides accurate language detection results.
• Supports a wide range of languages.

You can install required dependencies it using pip:

!pip install gcld3

import gcld3

def detect_language_gcld3(text):
    try:
        detector = gcld3.NNetLanguageIdentifier(min_num_bytes=0, max_num_bytes=1000)
        result = detector.FindLanguage(text=text)
        lang_detected = result.language
        return lang_detected
    except Exception as e:
        print("An error occurred:", e)
        return None

sample_text = "This text is written in English"
detected_language = detect_language_gcld3(sample_text)
print(f"Detected language: {detected_language}")

Pros:

• Fast and efficient language identification.
• Suitable for short text inputs.
• Developed by Google, known for quality NLP tools.

Cons:

• Does not provide translation capabilities.
• Requires installation of the gcld3 library.

Use Cases:

• Social media platforms for auto-detection of user-generated content languages.
• Customer support systems for identifying the language of incoming queries.

Conclusion: Gcld3 provides a quick and accurate solution for identifying the language of text inputs. It’s especially useful for scenarios where real-time language detection is essential, such as social media platforms and customer support systems.

Technique 4: FastText — Language Identification with Context

Overview: FastText is an extension of the Word2Vec model that excels in language identification. It considers subword information for improved accuracy.

Key Features:

• Efficient and accurate language identification.
• Incorporates subword information for better results.
• Suitable for various text lengths.
• Pre-trained models available for multiple languages.

You can install required dependencies it using pip.

! pip install fasttext

Download the pre-trained language identification model (lid.176.bin) from the specified URL using wget:

# Replace "Your_folder_path_to_download" with the actual path where you want to save the downloaded file.
!wget -O /"Your_folder_path_to_download"/lid.176.bin https://dl.fbaipublicfiles.com/fasttext/supervised-models/lid.176.bin

import fasttext

def detect_language_fasttext(text, model_path):
    try:
        model = fasttext.load_model(model_path)
        lang_detected = model.predict(text)
        return lang_detected[0][0].replace('__label__', '')
    except Exception as e:
        print("An error occurred:", e)
        return None

sample_text = "This is a sample text"
model_path = "path_to_pretrained_model.bin"
detected_language = detect_language_fasttext(sample_text, model_path)
print(f"Detected language: {detected_language}")

Pros:

• Incorporates subword information for enhanced accuracy.
• Provides models for a wide range of languages.
• Fast and efficient for language identification.

Cons:

• Requires downloading and loading a pre-trained model.
• May not perform well on very short text.

Use Cases:

• Online platforms for language-specific content filtering.
• Multilingual customer service systems.

Conclusion: FastText is a powerful technique for language identification, especially when considering subword information. It’s suitable for applications that require accurate and fast language detection.

Technique 5: spaCy — Multilingual NLP Library

Overview: spaCy is a robust NLP library that supports multiple languages. While it’s primarily known for tokenization and other NLP tasks, it can be used for language detection as well.

Key Features:

• Utilizes SpaCy’s powerful linguistic capabilities.
• Seamlessly integrates with SpaCy pipelines.
• Supports a wide range of languages.
• Lightweight and efficient for quick language identification.

You can install required dependencies it using pip:

!pip install spacy
!pip install spacy-language-detection

import spacy

def detect_language_spacy(text):
    try:
        nlp = spacy.load("en_core_web_sm")
        doc = nlp(text)
        detected_language = doc._.language['language']
        return detected_language
    except Exception as e:
        print("An error occurred:", e)
        return None

sample_text = "Konnichiwa, genki desu ka?"
detected_language = detect_language_spacy(sample_text)
print(f"Detected language: {detected_language}")

The below code showcases both document-level and sentence-level language detection using the trained SpaCy model.
Check the documentation for below code: https://pypi.org/project/spacy-language-detection/

import spacy
from spacy.language import Language

from spacy_language_detection import LanguageDetector


def get_lang_detector(nlp, name):
    return LanguageDetector(seed=42)  # We use the seed 42


nlp_model = spacy.load("en_core_web_sm")
Language.factory("language_detector", func=get_lang_detector)
nlp_model.add_pipe('language_detector', last=True)

# Document level language detection
job_title = "Senior NLP Research Engineer"
doc = nlp_model(job_title)
language = doc._.language
print(language)

# Sentence level language detection
text = "This is English text. Er lebt mit seinen Eltern und seiner Schwester in Berlin. Yo me divierto todos los días en el parque. Je m'appelle Angélica Summer, j'ai 12 ans et je suis canadienne."
doc = nlp_model(text)
for i, sent in enumerate(doc.sents):
    print(sent, sent._.language)

Pros:

• Enhances SpaCy’s capabilities with integrated language detection.
• Supports a wide range of languages.
• Provides lightweight and efficient language identification.
• Fits seamlessly into existing SpaCy workflows.

Cons:

• May not perform as accurately on very short or mixed-language texts.
• Limited to language detection and doesn’t provide additional NLP functionalities.

Use Cases:

• Integrating language detection into SpaCy-based NLP pipelines.
• Automating content processing based on detected language within SpaCy workflows.

Conclusion: spacy-language-detection extends the capabilities of SpaCy by incorporating efficient and accurate language identification. While it may not offer advanced NLP functionalities, it serves as a valuable tool for enhancing language-related tasks within SpaCy-based projects

Summary: Language detection is a crucial step in many NLP applications. In this guide, we explored the top 5 language detection techniques available in Python: Polyglot, Langdetect, Gcld3, FastText, and spaCy. Each technique has its strengths and is suitable for different use cases. By understanding these techniques, you’re better equipped to handle language diversity in your NLP projects.

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