Key Features of LughaatNLP

1. Tokenization

Accurate tokenization is a crucial first step in NLP pipelines, as it involves breaking down text into individual units (words, numbers, punctuations) for further processing. LughaatNLP’s tokenization module is designed to handle the intricacies of the Urdu script and language structure, ensuring precise tokenization of Urdu text.

Example:

Input: میرا نام نومان ہے
Output: ['میرا', 'نام', 'نومان', 'ہے']

2. Lemmatization

Lemmatization is the process of converting inflected words to their base or dictionary form. LughaatNLP’s lemmatization module enhances text analysis and comprehension by reducing the complexity of Urdu words, enabling a more accurate understanding of their meanings.

Example:

Input: کھاتے ہیں
Output: کھانا

3. Stop Word Removal

Stop words are common words that carry little to no semantic value, such as articles, prepositions, and conjunctions. LughaatNLP’s stop word removal module allows users to filter out these words from Urdu text, focusing the analysis on meaningful content.

Example:

Input: میں نے کتاب پڑھی اور اچھی لگی
Output: ['کتاب', 'پڑھی', 'اچھی', 'لگی']

4. Normalization

Urdu text often contains diacritics, character variations, and orthographic variations that can introduce noise and inconsistencies. LughaatNLP normalization module standardizes Urdu text by removing diacritics, normalizing character variations, handling common orthographic variations, and preserving special characters used in Urdu.

Example:

Input: بَاغ
Output: باغ

5. Stemming

Stemming is the process of reducing words to their root or stem form, which can be beneficial for various NLP tasks, such as information retrieval and text categorization. LughaatNLP stemming module improves text analysis and comprehension by extracting the stem forms of Urdu words.

Example:

Input: کھاتے
Output: کھا

6. Spell Checking

Misspelled words can introduce noise and errors in NLP systems. LughaatNLP spell checking module identifies and corrects misspelled words in Urdu text, enhancing text quality and readability.

Example:

Input: میری بیٹھی ہے
Output: میری بیٹی ہے

7. Part-of-Speech Tagging

Part-of-speech (POS) tagging is the process of assigning grammatical categories (e.g., nouns, verbs, adjectives) to words in text. LughaatNLP POS tagging module facilitates syntactic analysis and understanding of sentence structures in Urdu text, enabling more advanced NLP tasks.

Example:

Input: وہ کھیل رہا ہے
Output: [('وہ', 'PN'), ('کھیل', 'V'), ('رہا', 'AUX), ('ہے', 'AUX')]

8. Named Entity Recognition

Named entity recognition (NER) is the task of identifying and categorizing named entities, such as persons, organizations, and locations, within text. LughaatNLP’s NER module enables information extraction and semantic analysis of Urdu text by recognizing and classifying these entities.

Example:

Input: علی کراچی سے آیا
Output: [('علی', 'PERSON'), ('کراچی', 'LOCATION')]

In recent years, natural language processing (NLP) has witnessed tremendous growth. Now researchers and developers are exploring various languages beyond English. Urdu is one of the widely spoken languages in South Asia. To help with Urdu language processing tasks, a new and robust preprocessing library called LughaatNLP has arisen as a vital tool for researchers, developers, and language fans alike.