Metadata-Version: 2.4
Name: uznltk
Version: 0.0.13
Summary: The Uzbek Natural Language Toolkit (NLTK) is a Python package for natural language processing.
Home-page: https://github.com/UlugbekSalaev/uznltk
Author: Ulugbek Salaev
Author-email: ulugbek0302@gmail.com
License: MIT
Project-URL: Bug Tracker, https://github.com/UlugbekSalaev/uznltk/issues
Keywords: nltk,morphology,uzbek-language,pos tagging,morphological tagging
Classifier: Programming Language :: Python :: 3
Classifier: License :: OSI Approved :: MIT License
Classifier: Operating System :: OS Independent
Requires-Python: >=3.7
Description-Content-Type: text/markdown
License-File: LICENSE
Requires-Dist: requests
Requires-Dist: UzMorphAnalyser
Requires-Dist: UzSyllable
Dynamic: author
Dynamic: author-email
Dynamic: classifier
Dynamic: description
Dynamic: description-content-type
Dynamic: home-page
Dynamic: keywords
Dynamic: license
Dynamic: license-file
Dynamic: project-url
Dynamic: requires-dist
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# uznltk

**uznltk** — is a lightweight and convenient NLP (Natural Language Processing) library for the Uzbek language. It includes text cleaning, morphological analysis, number and text conversions, syllable splitting, and many other functions.

## 🔗 Links

- [PyPI page](https://pypi.org/project/uznltk)
- [GitHub page](https://github.com/UlugbekSalaev/uznltk)

## 👤 Authors

- **Salaev Ulug'bek** – ulugbek0302@gmail.com
- **Omanov Jasur** – jasuromonov77@gmail.com
- **Zaripboyev Ollabergan** – dewel000per@gmail.com

## 🔧 Install

```bash
pip install uznltk
```

## 🚀 Usage

```python
from uznltk import *
```

## 📚 Functions

### `clean_text(text)`

Corrects characters specific to the Uzbek language (g', o', ( ’ )).

```python
clean_text("O'zbekistonda ta'lim kuchli rivojlanmoqda")
# Natija: "O‘zbekistonda ta’lim kuchli rivojlanmoqda"
```

---

### `solid_sign(text)`

Returns words with a ( ’ ) character as a list.

```python
solid_sign("ta'lim bo'lishi oldindan ma'lum edi")
# Natija: ['ta’lim', 'ma’lum']
```

---

### `lemmatize(text)` and `stem_word(text)`

Extracts the stem of a word.

```python
lemmatize("mexanizatorlashtirilganlardan")
# Natija: "mexanizatorlashtirilgan"
```

---

### `number_to_text(number)`

Converts a number to Uzbek text.

```python
number_to_text(54)
# Natija: "ellik to‘rt"
```

---

### `text_to_number(text)`

Converts a number in text to numeric form.

```python
text_to_number("yetmish olti")
# Natija: 76
```

---

### `download(name)`

Downloads various resources (e.g. books, news).

```python
download("book")
```

---

### `clean_stopword(text)`

Removes stop words from the text.

```python
clean_stopword("salom dunyo, biz sen va u bilan bugun maktabga bordik")
# Natija: "salom dunyo, bugun maktabga bordik"
```

---

### `syllables(text)`

Divides words into syllables.

```python
syllables("Bizga ma’lum ishlar yuz bermoqda!")
# Natija: ['Biz-ga', 'ma’-lum', 'ish-lar', 'yuz', 'ber-moq-da!']
```

---

### `hyphenation(text)`

Each word is divided into syllables and presented in a list.

```python
hyphenation("salom dunyo")
# Natija: ['sa-lom dunyo', 'salom dun-yo']
```

---

### `count_syllable(text)`

Counts the number of syllables in the text.

```python
count_syllable("Salom Dunyo")
# Natija: 4
```

---

### `count_text(text)`

Counts the number of words in the text.

```python
count_text("Salom Dunyo")
# Natija: 2
```

---

### `split_sentences(text)`

Sorts the sentences in the text into lists.

```python
split_sentences("Salom Dunyo. Bugun ob-havo qisman bulutli")
# Natija: ['Salom Dunyo', 'Bugun ob-havo qisman bulutli']
```

---

### `split_words(text)`

Extracts only words from the text (without IP, email, emoji, URLs) into a list.

```python
split_words("sen 192.168.1.18 va helloworld@example.com elektron manzilidasan. Manba https://pypi.org")
# Natija: ['sen', 'va', 'elektron', 'manzilidasan', 'Manba']
```

---

## 💡 Information

- The library is entirely designed for **the Uzbek language**.
- It includes basic NLP components such as number processing, lemmatization, and syntacticization.

