Sentence Reduction for Syntactic Analysis of Compound Sentences in Punjabi Language

Abstract

This research paper is an attempt to develop a syntactic analysis system for compound sentences of Punjabi language. Methods/Statistical Analysis: Sentence simplification approach has been used for splitting compound sentences into simple sentences and then analyzing these simple sentences for syntactical error. A full form lexicon based morph, HMM based POS tagger and set of rules have been used for identification of grammatical mistakes. Findings: On testing an overall precision as 93.30, recall as 97.32 and F-measure as 95.25 is reported by the system. Application/Improvements: the system shows better performance on comparing it with existing Punjabi grammar (Precision=76.79, Recall=87.08) and Myanmar grammar checker that works on compound sentence and shows precision 83.75%.

Particle Swarm Optimization for Punjabi Text Summarization

“…(Singh et al, 2018) have elaborated suicide case of farmers in Punjab which is analyzed using morphological and sentiment analysis in the Gurmukhi script; later on, deep neural network is trained using feature set and splitter to split training and test sets. (Sharma, 2019) has developed syntactic analysis system in the Punjabi for language-based compound sentences. (Ahmad et al, 2020) have developed first-ever NER corpus for the Shahmukhi, another Punjabi script.…”