Syntactic parsing and supervised analysis of Sindhi text

This research study addresses the morphological and syntactic problems of Sindhi language text by proposing an Algorithm for tokenization and syntactic parsing. A Sindhi parser is developed on basis of proposed algorithm to perform syntactic parsing on Sindhi text using Sindhi WordNet (SWN) and corpus. Results of Sindhi syntactic parsing are accumulated to develop multi-class and multi-feature based Sindhi dataset in CSV format. Three attributes of Sindhi dataset are labelled as class. All three classes are comprised with different number of categories. SVM, Random forest and K-NN supervised machine learning methods are used and trained to analyze and evaluate the Sindhi dataset. 80% of dataset is used as training set and 20% of dataset is used as test set. In this research study, 10-fold cross validation technique is applied to evaluate and validate the supervised machine learning process. The SVM classifier gives better results on class phrase and UPOS whereas Random forest gives better result on class TagStatus. Precision, recall, f-measure and confusion matrix approve the performance of all supervised classifiers. The better performance of supervised machine learning methods, support the Sindhi dataset and Sindhi online parser for future research. This study opens new doors for research on right hand written languages especially Sindhi language to solve its computational linguistics problems.