Publication

Abstract : Part of speech tagging for Indian languages in general and Kannada in particular is not a very widely explored territory. There have been many attempts at developing a good POS tagger for Kannada, but the morphological complexity of the language makes it a hard nut to crack. Some of the best taggers available for Indian languages employ hybrids of machine learning or stochastic methods and linguistic knowledge. Though the results achieved using such methods are good, their practicability for other inflective Indian languages is reduced due to their heavy dependence on linguistic knowledge. Even though taggers can achieve very good results if provided good morphological information, the cost of creating these resources renders such methods impractical. In this paper, we present regular expression parts of speech tagger for Kannada. We apply 100 patterns incorporating the TDIL tags for Kannada and tested for accuracy with manual tagged corpus. © 2018, Springer Nature Singapore Pte Ltd.