An Analysis of Sindhi Annotated Corpus using Supervised Machine Learning Methods

Abstract

The linguistic corpus of Sindhi language is significant for computational linguistics process, machine learning process, language features identification and analysis, semantic and sentiment analysis, information retrieval and so on. There is little computational linguistics work done on Sindhi text whereas, English, Arabic, Urdu and some other languages are fully resourced computationally. The grammar and morphemes of these languages are analyzed properly using dissimilar machine learning methods. The development and research work regarding computational linguistics are in progress on Sindhi language at this time. This study is planned to develop the Sindhi annotated corpus using universal POS (Part of Speech) tag set and Sindhi POS tag set for the purpose of language features and variation analysis. The features are extracted using TF-IDF (Term Frequency and Inverse Document Frequency) technique. The supervised machine learning model is developed to assess the annotated corpus to know the grammatical annotation of Sindhi language. The model is trained with 80% of annotated corpus and tested with 20% of test set. The cross-validation technique with 10-folds is utilized to evaluate and validate the model. The results of model show the better performance of model as well as confirm the proper annotation to Sindhi corpus. This study described a number of research gaps to work more on topic modeling, language variation, sentiment and semantic analysis of Sindhi language.