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15 Μαΐ 2024 · 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. Table of Content. LughaatNLP. Key Features of LughaatNLP. 1. Tokenization. 2. Lemmatization. 3. Stop Word Removal. 4. Normalization. 5. Stemming. 6. Spell Checking. 7.
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We have presented a new dataset for question and answering models. Our dataset contains 27 different Urdu paragraphs which are taken from different available resources i.e Urdu Wikipedia, youtube and news articles etc.
19 Ιαν 2022 · This study presents the first version of the UNLT (Urdu Natural Language Toolkit) which contains three key text processing tools required for an Urdu NLP pipeline; word tokenizer, sentence...
1 Μαρ 2017 · The core objective of this paper is to present a survey regarding different linguistic resources that exist for Urdu language processing, to highlight different tasks in Urdu language...
23 Αυγ 2023 · Text preprocessing is a vital step in any natural language processing (NLP) pipeline. It involves cleaning and preparing raw text data before it can be used for various NLP tasks.
We use these representations to build state-of-the-art neural machine translation models for the Urdu language. These models exploit recent deep learning advancements like the encoder-decoder model, bi-directional encoders, and attention mechanisms to achieve state-of-the-art performance.
