The Development of a Morphosyntactic Tagset for Afrikaans and its Use with Statistical Tagging

In this paper, we present a morphosyntactic tagset for Afrikaans based on the guidelines developed by the Expert Advisory Group on Language Engineering Standards (EAGLES). We compare our slim yet expressive tagset, MAATS (Morphosyntactic AfrikAans TagSet), with an existing one which primarily focuses on a detailed morphosyntactic and semantic description of word forms. MAATS will primarily be used for the extraction of lexical data from large pos-tagged corpora. We not only focus on morphosyntactic properties but also on the processability with statistical tagging. We discuss the tagset design and motivate our classification of Afrikaans word forms, in particular we focus on the categorization of verbs and conjunctions. The complete tagset in presented and we briefly discuss each word class. In a case study with an Afrikaans newspaper corpus, we evaluate our tagset with four different statistical taggers. Despite a relatively small amount of training data, however with a large tagger lexicon, TnT-Tagger scores 97.05 % accuracy. Additionally, we present some error sources and discuss future work.