Research in Computing Science, Vol. 70, pp. 43-54, 2013.
Abstract: In this paper, we present a new approach to automatic tagging without requiring any machine learning algorithm or training data. We argue that the critical information required for tagging comes more from word internal structure than from the context and we show how a well designed morphological analyzer can assign correct tags and disambiguate many cases of tag ambiguities too. The crux of the approach is in the very definition of words. While others simply tokenize a given sentence based on spaces and take these tokens to be words, we argue that words need to be motivated from semantic and syntactic considerations, not orthographic conventions. We have worked on Telugu and Kannada languages and in this paper, we take the example of Telugu language and show how high quality tagging can be achieved with a fine grained, hierarchical tag set, carrying not only morpho-syntactic information but also some aspects of lexical and semantic information that is necessary or useful for syntactic parsing. In fact entire corpora can be tagged very fast and with a good degree of guarantee of quality. We give details of our experiments and results obtained. We believe our approach can also be applied to other languages.
Keywords: Tagging, Morphology, Part-Of-Speech, Lexicon, Hierarchical Tag Set, Telugu
PDF: A New Approach to Tagging in Indian Languages
PDF: A New Approach to Tagging in Indian Languages