Three data-driven publicly available part-of-speech taggers are applied to shallow parsing of Swedish texts. The phrase structure is represented by nine types of phrases in a hierarchical structure containing labels for every constituent type the token belongs to in the parse tree. The encoding is based on the concatenation of the phrase tags on the path from lowest to higher nodes. Various linguistic features are used in learning; the taggers are trained on the basis of lexical information only, part-of-speech only, and a combination of both, to predict the phrase structure of the tokens with or without part-of-speech. Special attention is directed to the taggers' sensitivity to different types of linguistic information included in learning, as well as the taggers' sensitivity to the size and the various types of training data sets. The method can be easily transferred to other languages.