TajPersParallelLexicalCorpus

A parallel lexical corpus for Tajik-Persian language pairs with usage examples and part-of-speech tagging.

📖 Description

This dataset contains parallel lexical pairs between Tajik and Persian languages, including usage examples and part-of-speech annotations. It serves as a valuable resource for linguistic research, machine translation, and natural language processing tasks involving Tajik and Persian languages.

👤 Author

Arabov Mullosharaf Kurbonovich
Creator and maintainer of the TajikPersianParallelLexicalCorpus

📊 Dataset Statistics

Overview

Metric	Value
Total Records	43,819
Unique Tajik Words	43,218
Unique Persian Words	40,435
Total Examples	22,635
Average Examples per Record	0.52

Data Quality

Metric	Count
Records with Empty Tajik	0
Records with Empty Persian	1,753
Records with Empty POS	64

Part of Speech Distribution

Part of Speech (Tajik)	English Translation	Count	Percentage
исм	Noun	24,179	55.18%
сифат	Adjective	15,609	35.62%
зарф	Adverb	1,548	3.53%
феъл	Verb	1,391	3.17%
исми хос	Proper Noun	443	1.01%
нидо	Interjection	246	0.56%
шумора	Numeral	146	0.33%
пайвандак	Conjunction	91	0.21%
ҷонишин	Pronoun	38	0.09%
ҳиссача	Particle	30	0.07%
пешоянд	Preposition	25	0.06%
пасоянд	Postposition	9	0.02%
TOTAL		43,819	100%

📁 Data Format

Each record contains the following fields:

Field	Type	Description
tajik	string	Word/phrase in Tajik language (Cyrillic script)
persian	string	Corresponding word/phrase in Persian language (Arabic script)
part_of_speech	string	Part of speech tag in Tajik (e.g., "исм" for noun, "феъл" for verb)
examples	list[string]	Usage examples with poetic and literary references from classical and modern Persian/Tajik literature
_queried_word	string	Original queried word (internal field used during data collection)

🎯 Intended Uses

This dataset is designed for the following NLP tasks:

Task Category	Description	Example Use Case
translation	Word-level translation between Tajik and Persian	Building a bilingual dictionary
token-classification	Part-of-speech tagging for Tajik and Persian	Training a POS tagger
fill-mask	Masked language modeling for bilingual contexts	Pretraining cross-lingual models
text-generation	Language modeling for Tajik and Persian	Generating Tajik/Persian text
sentence-similarity	Cross-lingual word embedding alignment	Learning word representations
feature-extraction	Extracting word features for downstream tasks	Creating word vectors

🚀 Usage Examples

Load the dataset

from datasets import load_dataset

# Load the dataset
dataset = load_dataset("TajikNLPWorld/TajPersParallelLexicalCorpus")

# Access the training split
train_data = dataset["train"]

# View first record
print(train_data[0])

# Filter by part of speech
nouns = train_data.filter(lambda x: x["part_of_speech"] == "исм")
print(f"Number of nouns: {len(nouns)}")

# Get statistics
print(f"Dataset size: {len(train_data)} records")
print(f"Unique Tajik words: {len(set(train_data['tajik']))}")

Example Record

{
    "tajik": "модар",
    "persian": "مادر",
    "part_of_speech": "исм",
    "examples": ["Ҷони киромӣ ба падар боздод, Қолбади тира ба модар супурд. - Рӯдакӣ"]
}

Translation Task

# Create a Tajik-Persian translation dictionary
translation_pairs = []
for item in train_data:
    if item['tajik'] and item['persian']:
        translation_pairs.append({
            'tajik': item['tajik'],
            'persian': item['persian']
        })

print(f"Created {len(translation_pairs)} translation pairs")

# Example usage
word = "модар"
matches = [pair for pair in translation_pairs if pair['tajik'] == word]
if matches:
    print(f"{word} -> {matches[0]['persian']}")

Token Classification (POS Tagging)

# Prepare data for POS tagging
pos_data = []
for item in train_data:
    if item['part_of_speech'] and item['tajik']:
        pos_data.append({
            'tokens': [item['tajik']],
            'pos_tags': [item['part_of_speech']]
        })

print(f"Prepared {len(pos_data)} examples for POS tagging")

# Analyze POS distribution
from collections import Counter
pos_counts = Counter([item['pos_tags'][0] for item in pos_data])
for pos, count in pos_counts.most_common(5):
    print(f"{pos}: {count}")

Fill-Mask / Language Modeling

# Prepare text for language modeling
lm_texts = []
for item in train_data:
    # Add word pairs
    if item['tajik'] and item['persian']:
        lm_texts.append(f"таджикӣ: {item['tajik']} форсӣ: {item['persian']}")
    
    # Add examples
    for example in item['examples']:
        if example:
            lm_texts.append(example)

print(f"Prepared {len(lm_texts)} texts for language modeling")
print(f"Sample: {lm_texts[0]}")

Sentence Similarity / Feature Extraction

# Create pairs for similarity learning
similar_pairs = []
for i, item in enumerate(train_data[:1000]):  # Limit for example
    if item['tajik'] and item['persian']:
        # Similar pair (same meaning)
        similar_pairs.append({
            'sentence1': item['tajik'],
            'sentence2': item['persian'],
            'label': 1.0  # similar
        })
        
        # Different pair (different meaning)
        if i > 0 and train_data[i-1]['tajik']:
            similar_pairs.append({
                'sentence1': item['tajik'],
                'sentence2': train_data[i-1]['tajik'],
                'label': 0.0  # not similar
            })

print(f"Created {len(similar_pairs)} pairs for similarity learning")

🔬 Research Applications

Research Area	How to Use This Dataset
Computational Linguistics	Study lexical similarities between Tajik and Persian
Machine Translation	Build and evaluate word-level translation models
Cross-lingual NLP	Develop multilingual word embeddings and representations
Morphological Analysis	Analyze word formation patterns in both languages
Lexical Semantics	Study semantic relations and word meanings

🌍 Languages

• Tajik (tg): Cyrillic script, variant of Persian spoken in Tajikistan
• Persian (fa): Arabic script, also known as Farsi, spoken in Iran, Afghanistan, and Tajikistan

📊 Dataset Creation

This dataset was created by:

1. Collecting parallel lexical pairs from various Tajik-Persian linguistic resources
2. Annotating parts of speech for each entry
3. Adding usage examples from classical and modern literature
4. Deduplicating and cleaning the data
5. Validating the quality of translations and annotations

📜 License

This dataset is released under the Apache 2.0 License. You are free to:

• Use the dataset for commercial and non-commercial purposes
• Modify and adapt the dataset
• Distribute copies and derivatives
• Use it for research and development

🤝 Citation

If you use this dataset in your research, please cite:

APA:

Arabov, M. K. (2026). TajPersParallelLexicalCorpus: A Parallel Lexical Corpus for Tajik-Persian [Data set]. Hugging Face. https://huggingface.co/datasets/TajikNLPWorld/TajPersParallelLexicalCorpus

BibTeX:

@dataset{arabov_tajpers_2026,
    title = {TajPersParallelLexicalCorpus: A Parallel Lexical Corpus for Tajik-Persian},
    author = {Arabov, Mullosharaf Kurbonovich},
    year = {2026},
    publisher = {Hugging Face},
    url = {https://huggingface.co/datasets/TajikNLPWorld/TajPersParallelLexicalCorpus}
}

📬 Contact

Author: Arabov Mullosharaf Kurbonovich
For questions, collaborations, or contributions:

• Hugging Face: TajikNLPWorld
• GitHub: TajikNLPWorld
• Email: [Add your email if you want]

🙏 Acknowledgments

Special thanks to:

• Contributors to Tajik and Persian lexical resources
• The Hugging Face team for the 🤗 Datasets library
• The open-source NLP community

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Dataset created and published by Arabov Mullosharaf Kurbonovich using Hugging Face 🤗 Datasets