Metadata-Version: 2.1
Name: vngrs-nlp
Version: 0.1.2
Summary: NLP Tools for Turkish Language.
Author: Meliksah Turker
Author-email: turkermeliksah@hotmail.com
License: GNU Affero General Public License v3.0
Platform: UNKNOWN
Classifier: Development Status :: 3 - Alpha
Classifier: Intended Audience :: Developers
Classifier: Intended Audience :: Education
Classifier: Intended Audience :: Science/Research
Classifier: Intended Audience :: Information Technology
Classifier: Topic :: Scientific/Engineering
Classifier: Topic :: Scientific/Engineering :: Artificial Intelligence
Classifier: Topic :: Scientific/Engineering :: Information Analysis
Classifier: Topic :: Text Processing
Classifier: Topic :: Text Processing :: Linguistic
Classifier: Topic :: Software Development
Classifier: Topic :: Software Development :: Libraries
Classifier: Programming Language :: Python :: 3.6
Classifier: Programming Language :: Python :: 3.7
Classifier: Programming Language :: Python :: 3.8
Classifier: Programming Language :: Python :: 3.9
Description-Content-Type: text/markdown
Provides-Extra: extras
License-File: LICENSE

![](https://github.com/vngrs-ai/VNLP/blob/main/img/logo.png)
## VNLP: Turkish NLP Tools
State of the art, lightweight NLP tools for Turkish language.
Developed by VNGRS.
https://vngrs.com/
### Functionality:
- Sentence Splitter
- Normalizer
	- Spelling/Typo correction
	- Convert numbers to word form
	- Deasciification
- Stopword Remover:
	- Static
	- Dynamic
- Stemmer: Morphological Analyzer & Disambiguator
- Named Entity Recognizer (NER) 
- Dependency Parser
- Part of Speech (POS) Tagger
- Sentiment Analyzer
- Turkish Word Embeddings
	- FastText
	- Word2Vec
- Text Summarization: In development progress...
### Installation
```
pip install vngrs-nlp
```
```
conda install vngrs-nlp
```
### Documentation:
- Detailed documentation about usage, classes, functions, datasets and evaluation metrics are available at [Documentation](https://vnlp.readthedocs.io).
### Usage Example:
**Dependency Parser**
```
from vnlp import DependencyParser
dep_parser = DependencyParser()
dep_parser.predict("Onun iÃ§in yol arkadaÅŸlarÄ±mÄ±zÄ± titizlikle seÃ§er, kendilerini iyice sÄ±narÄ±z.")
[(1, 'Onun', 5, 'obl'),
(2, 'iÃ§in', 1, 'case'),
(3, 'yol', 1, 'nmod'),
(4, 'arkadaÅŸlarÄ±mÄ±zÄ±', 5, 'obj'),
(5, 'titizlikle', 6, 'obl'),
(6, 'seÃ§er', 7, 'acl'),
(7, ',', 10, 'punct'),
(8, 'kendilerini', 10, 'obj'),
(9, 'iyice', 8, 'advmod'),
(10, 'sÄ±narÄ±z', 0, 'root'),
(11, '.', 10, 'punct')]
# Spacy's submodule Displacy can be used to visualize DependencyParser result.
import spacy
from vnlp import DependencyParser
dependency_parser = DependencyParser()
result = dependency_parser.predict("OÄŸuz'un kÄ±rmÄ±zÄ± bir Astra'sÄ± vardÄ±.", displacy_format = True)
spacy.displacy.render(result, style="dep", manual = True)
```
![](https://github.com/vngrs-ai/VNLP/blob/main/img/dp_vis_sample.png)

