Probabilistic tagging of minority language data: a case study using Qtag

While probabilistic methods of part-of-speech tag assignment have long received consideration in corpus and computational-linguistic research, less attention would appear to have been paid to date to the development of tagging accuracy over rounds of iterative, interactive training in applications of these methods. Understanding this aspect of probabilistic tagging is arguably of particular importance to the successful construction of minority language corpora, where financial resources for corpus development are often limited and no fixed standards for either orthography or part of speech assignment may necessarily exist. This paper therefore presents a case study in the application of pure probabilistic tagging, as represented by Qtag (Tufis and Mason, 1998), to minoritylanguage data from Mennonite Low German (Plautdietsch). Concentrating upon the relationship of several factors (including training data size, tag set complexity, and orthographic normalization) to the development of tagging accuracy, the present study conducts computational simulations of the iterative, interactive training process to compare the interactions of these factors quantitatively over time. The study concludes with a discussion of these factors’ relevance to the development of accuracy in tagging as well as of potential confounds to the application of probabilistic tagging methods to similar minority language data.