The Urdu alphabet is the right-to-left alphabet used for the Urdu language. It is a modification of the Persian alphabet known as Perso-Arabic, which is itself a derivative of the Arabic alphabet. The Urdu alphabet has up to 58 letters with 39 basic letters and no distinct letter cases, the Urdu alphabet is typically written in the calligraphic Nastaʿlīq script.
46 Alphabets, 10 Digits, 6 Punctuations, 6 Diacritics.
The normalization of Urdu text is necessary to make it useful for the machine
learning tasks. In the
normalization module, the very basic
problems faced when working with Urdu data are handled with ease and
efficiency. All the problems and how
normalization module handles
them are listed below.
This modules fixes the problem of correct encodings for the Urdu characters as well as replace Arabic characters with correct Urdu characters. This module brings all the characters in the specified unicode range (0600-06FF) for Urdu language.
It also fixes the problem of joining of different Urdu words. By joining we mean that when space between two Urdu words is removed, they must not make a new word. Their rendering must not change and even after the removal of space they should look the same.
>>> from urduhack import normalize >>> text = normalize(text)
Stop words are natural language words which have very little meaning, such as “and”, “the”, “a”, “an”, and similar words. These words are highly redundant in texts and do not contribute much so it is sometimes a viable approach to remove the stop words in pre-processing of the data.
>>> from urduhack.stop_words import STOP_WORDS, remove_stopwords >>> print(STOP_WORDS) >>> text = remove_stopwords(text)
This module is another crucial part of the Urduhack. This module performs tokenization on sentence. It separates different sentence from each other and converts each string into a complete sentence token. Note here you must not confuse yourself with the word token. They are two completely different things.
This library provides state of art word tokenizer for Urdu Language. It takes care of the spaces and where to connect two urdu characters and where not to.
The tokenization of Urdu text is necessary to make it useful for the NLP tasks. This module provides the following functionality:
- Sentence Tokenization
- Word Tokenization
The tokenization of Urdu text is necessary to make it useful for the machine
learning tasks. In the
tokenization module, we solved the problem related to
sentence and word tokenization.