uznltk 0.0.9

The Uzbek Natural Language Toolkit (NLTK) is a Python package for natural language processing.

uznltk

https://pypi.org/project/uznltk
https://github.com/UlugbekSalaev/uznltk

uznltk is Uzbek Natural Language ToolKit
It is created as a python library and uploaded to PyPI. It is simply easy to use in your python project or other programming language projects via the API.

About project

The Natural Language Toolkit (NLTK) is a Python package for natural language processing.

Quick links

• Github
• PyPI
• Web-UI

Demo

You can use web interface.

Features

• Corpus
• Morphological annotated dataset
• Help function

uznltk

Natural Language Toolkit for Uzbek O‘zbek tili uchun NLP kutubxonasi

Function

• Tokenization into words
• Sentence segmentation
• Stop-word identification
• Normalization of apostrophes in text
• Extraction of words with punctuation marks

Installatoin

pip install uznltk

## Usage

Three options to run uznltk:

- pip
- API 
- Web interface

### pip installation

To install uznltk, simply run:

```code
pip install uznltk

After installation, use in python like following:

# import the library
from uznltk import Tagger
# create an object 
tagger = Tagger()
# call tagging method
tagger.pos_tag('Bizlar bugun maktabga bormoqchimiz.')
# output
[('Bizlar','NOUN'),('bugun', 'NOUN'), ('maktabga', 'NOUN'), ('bormoqchimiz', 'VERB'), ('.', 'PUNC')]

API

API configurations:

• Method: GET
• Response type: string
• URL: https://nlp.urdu.uz:8080/uznltk/pos_tag
  • Parameters: text:string
• Sample Request: https://nlp.urdu.uz:8080/uznltk/pos_tag?text=Ular%20maktabga%20borayaptilar.
• Sample output: [("Ular","NOUN"),("maktabga",""),("borayaptilar",""),(".","PUNC")]

Web-UI

The web interface created to use easily the library: You can use web interface here.

POS tag list

Tagger using following options as POS tag:
NOUN Noun {Ot}
VERB Verb {Fe'l}
ADJ Adjective {Sifat}
NUM Numeric {Son}
ADV Adverb {Ravish}
PRN Pronoun {Olmosh}
CNJ Conjunction {Bog'lovchi}
ADP Adposition {Ko'makchi}
PRT Particle {Yuklama}
INTJ Interjection {Undov}
MOD Modal {Modal}
IMIT Imitation {Taqlid}
AUX Auxiliary verb {Yordamchi fe'l}
PPN Proper noun {Atoqli ot}
PUNC Punctuation {Tinish belgi}
SYM Symbol {Belgi}

Result Explaining

The method pos_tag returns list, that an item of the list contain tuples for each token of the text with following format: (token, pos), for POS tag list, see POS Tag List section on above.

Result from tagger method

[('Bizlar','NOUN'),('bugun', 'NOUN'), ('maktabga', 'NOUN'), ('bormoqchimiz', 'VERB'), ('.', 'PUNC')]

Documentation

See here.

Citation

@article{10.1063/5.0241461,
    author = {Salaev, Ulugbek},
    title = {UzMorphAnalyser: A morphological analysis model for the Uzbek language using inflectional endings},
    journal = {AIP Conference Proceedings},
    volume = {3244},
    number = {1},
    pages = {030058},
    year = {2024},
    month = {11},
    abstract = {As Uzbek language is agglutinative, has many morphological features which words formed by combining root and affixes. Affixes play an important role in the morphological analysis of words, by adding additional meanings and grammatical functions to words. Inflectional endings are utilized to express various morphological features within the language. This feature introduces numerous possibilities for word endings, thereby significantly expanding the word vocabulary and exacerbating issues related to data sparsity in statistical models. This paper present modeling of the morphological analysis of Uzbek words, including stemming, lemmatizing, and the extraction of morphological information while considering morpho-phonetic exceptions. Main steps of the model involve developing a complete set of word-ending with assigned morphological information, and additional datasets for morphological analysis. The proposed model was evaluated using a curated test set comprising 5.3K words. Through manual verification of stemming, lemmatizing, and morphological feature corrections carried out by linguistic specialists, it obtained a word-level accuracy of over 91\%. The developed tool based on the proposed model is available as a web-based application and an open-source Python library.},
    issn = {0094-243X},
    doi = {10.1063/5.0241461},
    url = {https://doi.org/10.1063/5.0241461},
    eprint = {https://pubs.aip.org/aip/acp/article-pdf/doi/10.1063/5.0241461/20272108/030058\_1\_5.0241461.pdf},
}

Contact

For help and feedback, please feel free to contact the author.