--- language: - tg license: apache-2.0 tags: - part-of-speech - pos-tagging - tajik-language - lexical-resource - nlp datasets: - tajik-pos-corpus --- # 🇹🇯 Tajik POS Corpus A comprehensive part-of-speech tagged lexicon for the Tajik language, containing over **52,508** unique word forms with their morphological categories. ## 📖 Description This dataset is a curated collection of Tajik words annotated with part-of-speech tags according to standard Tajik grammatical classification. It includes both general vocabulary and proper nouns (including toponyms), and serves as a fundamental resource for: - Training and evaluating POS taggers for Tajik - Morphological analysis and lemmatization - Lexicographic research - Language learning applications The corpus has been carefully cleaned: duplicates removed, artifacts (like `_1` suffixes) eliminated, and enclitics category verified against academic grammar. ## 📊 Dataset Statistics ### Overall Metrics | Metric | Value | |--------|-------| | Total Records | 52,508 | | Unique Tajik Words | 52,447 | | Uniqueness Ratio | 99.88% | | Unique POS Tags | 28 | ### Word Length Statistics | Metric | Value | |--------|-------| | Average Length | 7.85 characters | | Median Length | 7 | | Minimum Length | 1 | | Maximum Length | 70 | ### Part-of-Speech Distribution | Part of Speech | Count | Percentage | |:-----------------|:--------|:-------------| | исм | 23,730 | 45.19% | | сифат | 15,437 | 29.40% | | исми хос | 8,894 | 16.94% | | зарф | 1,525 | 2.90% | | феъл | 1,345 | 2.56% | | нидо | 248 | 0.47% | | шумора | 139 | 0.26% | | символ | 102 | 0.19% | | ихтисора | 100 | 0.19% | | рақам | 100 | 0.19% | | пайвандак | 86 | 0.16% | | пешоянд | 72 | 0.14% | | пасванд | 70 | 0.13% | | ҷонишин | 68 | 0.13% | | феъли ёридиҳанда | 63 | 0.12% | | замони феъл | 62 | 0.12% | | ибора | 61 | 0.12% | | ҳиссача | 53 | 0.10% | | пешванд | 51 | 0.10% | | воҳиди ченак | 50 | 0.10% | | муайянкунанда | 44 | 0.08% | | нишондиҳанда | 40 | 0.08% | | модалӣ | 39 | 0.07% | | тақлидӣ | 37 | 0.07% | | пайванди табей | 30 | 0.06% | | пайванди бабей | 26 | 0.05% | | пасоянд | 23 | 0.04% | | энклитика | 13 | 0.02% | ### 🔝 Top 10 Longest Words | # | Word | POS | Length | |----:|:-----------------------------------------------------------------------|:---------|---------:| | 1 | Боғи фарҳангӣ-фароғатии марказии Кӯлоб ба номи Мир Сайид Алии Ҳамадонӣ | исми хос | 70 | | 2 | Осорхонаи мероси хаттӣ ва адабии ба номи Мир Сайид Алии Ҳамадонӣ | исми хос | 64 | | 3 | Театри давлатии академии опера ва балети ба номи Садриддин Айнӣ | исми хос | 63 | | 4 | Филиали Донишгоҳи байналмилалии сайёҳӣ ва соҳибкории Тоҷикистон | исми хос | 63 | | 5 | Осорхонаи санъати миллии Тоҷикистон ба номи Камолиддин Беҳзод | исми хос | 61 | | 6 | Кумитаи иҷроияи Ҳизби Халқии Демократии Тоҷикистон дар Кӯлоб | исми хос | 60 | | 7 | Масҷиди марказии ҷомеи Душанбе ба номи Абуҳанифа Имоми Аъзам | исми хос | 60 | | 8 | Театри давлатии академии драмавии ба номи Абулқосим Лоҳутӣ | исми хос | 58 | | 9 | Коллеҷи санъати ҷумҳуриявии ба номи Кароматулло Қурбонов | исми хос | 56 | | 10 | Театри давлатии академии опера ва балети ба номи С. Айнӣ | исми хос | 56 | ### 📝 First Letter Distribution (Top 10) | First Letter | Count | Percentage | |:---------------|:--------|:-------------| | б | 4,303 | 8.19% | | м | 4,152 | 7.91% | | с | 3,353 | 6.39% | | н | 2,486 | 4.73% | | д | 2,376 | 4.53% | | т | 2,301 | 4.38% | | а | 2,273 | 4.33% | | к | 2,105 | 4.01% | | п | 2,076 | 3.95% | | х | 1,743 | 3.32% | ### 🔤 Special Words - **Words with spaces** (multi-word expressions): 5 examples (total 5) - **Words with hyphens**: 5 examples ## 📁 Data Format Each record is a JSON object with two fields: | Field | Type | Description | Example | |-------|------|-------------|---------| | `tajik` | string | The word form in Tajik (Cyrillic script) | `"Рунҷ"` | | `part_of_speech` | string | The part-of-speech tag | `"исми хос"` | ### Example Record ```json { "tajik": "Рунҷ", "part_of_speech": "исми хос" } ``` ## 🚀 Usage ### Load with 🤗 Datasets ```python from datasets import load_dataset dataset = load_dataset("TajikNLPWorld/tajik-pos-corpus") train_data = dataset["train"] # Filter nouns nouns = train_data.filter(lambda x: x["part_of_speech"] == "исм") print(f"Number of nouns: {len(nouns)}") ``` ## 📜 License Apache License 2.0 ## 🤝 Citation ``` @dataset{tajik_pos_corpus, title = {Tajik POS Corpus}, author = {Arabov Mullosharaf Kurbonovich, TajikNLPWorld}, year = {2025}, publisher = {Hugging Face}, url = {https://huggingface.co/datasets/TajikNLPWorld/tajik-pos-corpus} } ``` --- *Generated with ❤️ for the Tajik NLP community*