[Megyesi(2002)] retrained three POS taggers for shallow parsing. Unlike the other papers, she dealt with shallow parsing for Swedish, and not English.
Experimental results showed that, again, when using POS taggers as the basis of shallow parsers, careful consideration needs to be given to how the task is to be encoded (choice of features). Unlike other studies, the author found that ignoring lexical information improved performance for all her systems. It is unclear whether this is due to linguistic differences between English and Swedish, or else due to the fact that some of her POS taggers were built with English in mind.
The shallow parsers were then trained on varying amounts of training data for each task. Unsurprisingly performance improved with the amount of training data in each case. However, no shallow parser yielded uniformly superior results to any other shallow parser.