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Europarl Parallel Corpus

The Europarl Parallel Corpus contains proceedings from the European Parliament, providing high-quality multilingual text data. This corpus is widely used in NLP research for machine translation and language identification tasks.
Source: Europarl Parallel CorpusDomain: Parliamentary proceedings from the European UnionQuality: Professional translations and transcriptions with formal, structured language

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Dataset Structure

Our processed dataset contains:

Total Samples

49,000 text samples (7,000 per language)

Languages

7 European languages

Balance

Perfectly balanced with equal samples per language

Format

CSV with text and language label columns

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File Format

The dataset is stored as europarl_multilang_dataset_7000.csv with two columns: 
texto,idioma
Reanudación del período de sesiones,es
"(El Parlamento, de pie, guarda un minuto de silencio)",es
Wiederaufnahme der Sitzungsperiode,de
"Ich bitte Sie, sich zu einer Schweigeminute zu erheben.",de
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texto
string
required
The text content in the source language
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idioma
string
required
Two-letter ISO language code (es, de, fr, it, nl, pt, sv)

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Languages Included

The dataset covers 7 European languages representing different language families:
Romance language - Examples:
  • “Reanudación del período de sesiones”
  • “Señora Presidenta, una cuestión de procedimiento.”
Spanish uses characteristic articles (el, la) and verb conjugations that make it highly distinguishable.
Germanic language - Examples:
  • “Wiederaufnahme der Sitzungsperiode”
  • “Frau Präsidentin, eine Frage zur Geschäftsordnung.”
German features compound words and distinctive articles (der, die, das).
Romance language - Examples:
  • “Reprise de la session”
  • “Madame la Présidente, c’est une motion de procédure.”
French uses accented characters (é, è, ê) and articles (le, la, les).
Romance language - Examples:
  • “Ripresa della sessione”
  • “Signora Presidente, è una questione procedurale.”
Italian features distinctive endings (-zione, -ità) and double consonants.
Germanic language - Examples:
  • “Hervatting van de zitting”
  • “Mevrouw de Voorzitter, dit is een motie van orde.”
Dutch uses distinctive digraphs (ij, oe) and articles (de, het).
Romance language - Examples:
  • “Reinício da sessão”
  • “Senhora Presidente, é uma questão de ordem.”
Portuguese features tildes (ã, õ) and distinctive verb forms.
Germanic language - Examples:
  • “Återupptagande av sessionen”
  • “Fru talman, det här är en ordningsfråga.”
Swedish uses unique characters (å, ä, ö) and compound words.

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Dataset Characteristics

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Text Length Distribution

Parliamentary proceedings contain varied text lengths:
  • Short statements: 5-20 words (e.g., procedural statements)
  • Medium sentences: 20-50 words (typical parliamentary remarks)
  • Long passages: 50+ words (detailed explanations and arguments)
The variety in text length ensures the model learns to identify languages from both short and long text samples, improving robustness.

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Domain-Specific Vocabulary

The parliamentary domain includes:

Political Terms

Commission, Parliament, Council, Member States

Procedural Language

Amendments, resolutions, votes, questions

Formal Register

Titles, formal addresses, official terminology

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Data Quality

Professional transcriptions - High accuracy and proper formatting
Balanced representation - Equal samples prevent class imbalance
Clean text - Minimal noise, consistent structure
Parallel content - Same topics across languages aid comparison

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Loading the Dataset

The dataset is loaded as a pandas DataFrame: 
import pandas as pd

# Load the dataset
df = pd.read_csv('dataset/europarl_multilang_dataset_7000.csv')

# Display basic information
print(f"Total samples: {len(df)}")
print(f"\nLanguage distribution:")
print(df['idioma'].value_counts())
Expected output: 
Total samples: 49000

Language distribution:
de    7000
es    7000
fr    7000
it    7000
nl    7000
pt    7000
sv    7000
Name: idioma, dtype: int64
The perfect balance across languages eliminates the need for stratified sampling or class weighting during model training.

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Why Europarl?

The Europarl corpus offers several advantages for language detection:
  1. Multilingual by Design: All languages cover similar topics, making linguistic features the primary distinguishing factor
  2. High Quality: Professional translations ensure grammatical correctness and proper language use
  3. Formal Register: Consistent formality level reduces stylistic variation
  4. Representative: Covers political, economic, and social topics with rich vocabulary
While Europarl is excellent for training, the formal parliamentary style may perform differently on informal text (social media, colloquial speech). Consider domain adaptation for production use.

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Next Steps

Preprocessing

Learn how the text is cleaned and prepared

Vectorization

Discover how text becomes numerical features

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