Prediction of Part of Speech Tags for Punjabi using Support Vector Machines

摘要

Part-Of-Speech (POS) tagging is a task of assigning the appropriate POS or lexical category to each word in a natural language sentence. In this paper, we have worked on automated annotation of POS tags for Punjabi. We have collected a corpus of around 27,000 words, which included the text from various stories, essays, day-to-day conversations, poems etc., and divided these words into different size files for training and testing purposes. In our approach, we have used Support Vector Machine (SVM) for tagging Punjabi sentences. To the best of our knowledge, SVMs have never been used for tagging Punjabi text. The result shows that SVM based tagger has outperformed the existing taggers. In the existing POS taggers of Punjabi, the accuracy of POS tagging for unknown words is less than that for known words. But in our proposed tagger, high accuracy has been achieved for unknown and ambiguous words. The average accuracy of our tagger is 89.86%, which is better than the existing approaches.