Tagger Voting for Urdu
Bushra Jawaid, Ondřej Bojar · 2012
In this paper, we focus on improving part-of-speech (POS) tagging for Urdu by using exist-ing tools and data for the language. In our experiments, we use Humayoun’s morphological analyzer, the POS tagging module of an Urdu Shallow Parser and our own SVM Tool tag-ger trained on CRULP manually annotated data. We convert the output of the taggers to a common format and more importantly unify their tagsets. On an independent test set, our tagger outperforms the other tools by far. We gain some further improvement by implementing a voting strategy that allows us to consider not only our tagger but also include suggestions by the other tools. The final tagger reaches the accuracy of 87.98%.