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Using Sentence Simplification to Generate Paraphrase for Low Resource Punjabi Language


Affiliations
1 Research Scholar, SBBS University, Jalandhar, India
2 Associate Professor, DAV University, Jalandhar, India
 

The field of natural language processing is growing in computer science, and generating paraphrases is a difficult task, especially for languages like Hindi, Punjabi, and Urdu, which are morphologically rich and have limited resources. This research article focuses on generating paraphrases for Punjabi, a morphologically rich Indian language, using a sentence simplification approach. The author employed several sentence simplification algorithms to simplify long Punjabi sentences and used antonym-synonym replacement to generate the paraphrases. The sentence simplification component of the system achieved a precision of 100%, recall of 95%, and an f-measure of 97.43% when tested with a set of data. The developed system's performance was analyzed using various complexity measurement parameters, and it was observed that a combination of lexical and syntactic simplifications yielded the best results.

Keywords

NLP, Punjabi Language Processing, Paraphrasing, Syntactic Simplification, Lexical Simplification.
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  • Using Sentence Simplification to Generate Paraphrase for Low Resource Punjabi Language

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Authors

Ravinder Mohan Jindal
Research Scholar, SBBS University, Jalandhar, India
Leekha Jindal
Research Scholar, SBBS University, Jalandhar, India
Sanjeev Kumar Sharma
Associate Professor, DAV University, Jalandhar, India

Abstract


The field of natural language processing is growing in computer science, and generating paraphrases is a difficult task, especially for languages like Hindi, Punjabi, and Urdu, which are morphologically rich and have limited resources. This research article focuses on generating paraphrases for Punjabi, a morphologically rich Indian language, using a sentence simplification approach. The author employed several sentence simplification algorithms to simplify long Punjabi sentences and used antonym-synonym replacement to generate the paraphrases. The sentence simplification component of the system achieved a precision of 100%, recall of 95%, and an f-measure of 97.43% when tested with a set of data. The developed system's performance was analyzed using various complexity measurement parameters, and it was observed that a combination of lexical and syntactic simplifications yielded the best results.

Keywords


NLP, Punjabi Language Processing, Paraphrasing, Syntactic Simplification, Lexical Simplification.

References