Automic Paraphras Generation for Urdu Language

Abstract

Automatic paraphrase generation is an important task in Natural Language Processing (NLP) that focuses on producing alternative sentences with the same meaning as the original text. This study aims to develop an automatic paraphrase generation system specifically for the Urdu language. Due to the limited availability of linguistic resources and computational tools for Urdu, generating accurate paraphrases remains a challenging problem.

The research explores different NLP and machine learning techniques to generate meaningful paraphrases while preserving the original context and semantics. A dataset of Urdu sentences is used to train and evaluate the proposed model. Various approaches such as rule-based methods, statistical techniques, and deep learning models are analyzed to improve paraphrasing quality.

The results demonstrate that the proposed system can effectively generate alternative Urdu sentences while maintaining the original meaning. This study contributes to the advancement of Urdu language processing and can support applications such as text summarization, question answering, machine translation, and plagiarism detection.