Prediction of part of speech tags for Punjabi using support vector machines
Joint Authors
Source
The International Arab Journal of Information Technology
Issue
Vol. 13, Issue 6 (31 Dec. 2016)6 p.
Publisher
Publication Date
2016-12-31
Country of Publication
Jordan
No. of Pages
6
Main Subjects
Information Technology and Computer Science
Topics
Abstract EN
Part-of-Speech (POS) tagging is a task of assigning the appropriate POS or l^^ical 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 89.8 which is better than the existing approaches.
American Psychological Association (APA)
Kumar, Dinesh& Josan, Gurpreet. 2016. Prediction of part of speech tags for Punjabi using support vector machines. The International Arab Journal of Information Technology،Vol. 13, no. 6.
https://search.emarefa.net/detail/BIM-654819
Modern Language Association (MLA)
Kumar, Dinesh& Josan, Gurpreet. Prediction of part of speech tags for Punjabi using support vector machines. The International Arab Journal of Information Technology Vol. 13, no. 6 (Dec. 2016).
https://search.emarefa.net/detail/BIM-654819
American Medical Association (AMA)
Kumar, Dinesh& Josan, Gurpreet. Prediction of part of speech tags for Punjabi using support vector machines. The International Arab Journal of Information Technology. 2016. Vol. 13, no. 6.
https://search.emarefa.net/detail/BIM-654819
Data Type
Journal Articles
Language
English
Notes
Includes appendix.
Record ID
BIM-654819