Combining Language Independent Part-of-Speech Tagging Tools
Authors
Publication date
1 January 2013
Publisher
OASIcs - OpenAccess Series in Informatics. 2nd Symposium on Languages, Applications and Technologies
Doi
Abstract
Part-of-speech tagging is a fundamental task of natural language processing. For languages with a very rich agglutinating morphology, generic PoS tagging algorithms do not yield very high accuracy due to data sparseness issues. Though integrating a morphological analyzer can efficiently solve this problem, this is a resource-intensive solution. In this paper we show a method of combining language independent statistical solutions -- including a statistical machine translation tool -- of PoS-tagging to effectively boost tagging accuracy. Our experiments show that, using the same training set, our combination of language independent tools yield an accuracy that approaches that of a language dependent system with an integrated morphological analyzer