Grammatical error detection and correction model for Sinhala language sentences

As the national language of Sri Lanka, the greater part of the exercises at most of all the services are completed in Sinhala whereas it is imperative to guarantee the spelling and syntactic accuracy to convey the ideal significance from the perspective of automated materials with the unavailability of resources even though there are enough amount of available materials as hard copy and books. With the high multifaceted nature of the language, it sets aside extensive effort to physically edit the substance of a composed setting. The necessity to overcome this problem has risen numerous years back. But with the complexity of grammar rules in morphologically lavish Sinhala language, the accuracy of the grammar checkers developed so far has been contrastingly lower and thus, to overcome the issue a novel hybrid approach has been introduced. Spell checked Sinhala active sentences being preprocessed, separated nouns and verbs were analyzed with the help of a resourceful part-of-speech-tagger and a morphological analyzer and alongside the sentences were sent through a pattern recognition mechanism to identify its sentence pattern. Then a decision tree-based algorithm has been used to evaluate the verb with the “subject” and output feedback about the correctness of the sentence. To train this decision tree, a dataset consisting of 800 records which included information about 25 predefined grammar rules in Sinhala was used. Finally, the error correction was provided using a machine learning algorithm-based sentence guessing model for the three possible tenses. Conducted research results paved the way to identify the sentence pattern, grammar rules and finally, suggest corrections for identified incorrect grammatical sentences with an acceptable accuracy rate of 88.6 percent which concluded that the proposed hybrid approach was an accurate approach for detecting and correcting grammatical mistakes in Sinhala text.