Persian POS tagging using probabilistic morphological analysis Online publication date: Sat, 07-Aug-2010
by Hakimeh Fadaei, Mehrnoush Shamsfard
International Journal of Computer Applications in Technology (IJCAT), Vol. 38, No. 4, 2010
Abstract: Part of speech (POS) tagging as a fundamental task in natural language processing (NLP) has attracted many research efforts and many taggers are developed with different approaches to reach high performance and accuracy. In many complex applications of NLP, tagged corpora are among essential resources and designing an algorithm to create or enrich these resources is of high importance. Handling unknown words is a challenge in POS tagging which usually decreases the performance of taggers. This paper presents a POS tagger for Persian. It exploits a hybrid approach which is a combination of statistical and rule-based methods to tag Persian sentences. The proposed tagger uses a novel probabilistic morphological analysis to tag unknown words. As a secondary result of this research a knowledge base of Persian morphological rules with their probabilities is built according to a corpus. Experimental results show that our method improves the tagging performance and accuracy.
Existing subscribers:
Go to Inderscience Online Journals to access the Full Text of this article.
If you are not a subscriber and you just want to read the full contents of this article, buy online access here.Complimentary Subscribers, Editors or Members of the Editorial Board of the International Journal of Computer Applications in Technology (IJCAT):
Login with your Inderscience username and password:
Want to subscribe?
A subscription gives you complete access to all articles in the current issue, as well as to all articles in the previous three years (where applicable). See our Orders page to subscribe.
If you still need assistance, please email subs@inderscience.com
Our Blog 
Follow us on Twitter 
Visit us on Facebook