FarsiTag: A part-of-speech tagging system for Persian

摘要

FarsiTag is a tagging system capable of assigning the most probable part-of-speech (POS) tags to Persian words in a text. In this system, some linguistic rules have been used to select the best POS tag for every Persian word. The present study aims to report the processes during which a robust tagging system-FarsiTag-was designed and implemented on Persian texts. A POS-tagged parallel corpus of English-Persian containing about 5,000,000 words has also been developed as a side-product of the mentioned tagger. An experiment has been conducted to evaluate the performance of the system while tagging unrestricted Persian texts. The highest rate of error traces back to medical and religious genres, while the lowest system error type is related to the scientific texts. The total error rate considering all domains is as low as 1.4%, with the overall system accuracy of 98.6% which is very promising for a language like Persian.