This paper is about two aspects of subcategorisation in NLP. First, it is about the automatic extraction of subcategorisation information from corpora. More specifically, we are concerned with unsupervised learning of subcategorisation information from tagged text by means of hierarchical clustering. The second aspect of the paper is the usage of this subcategorisation information for parsing, especially for the distinction between complements and adjuncts. We show that the information learned by unsupervised clustering can be exploited by a memory-based learner, to improve upon the complement-adjunct distinction. We compare the improvement gained by the use of this unsupervised information (1%) to that of different representations of subcategorisation information extracted from the tree-bank annotation (maximum 1.5%). The unsupervised information thus achieves two thirds of the improvement that can be obtained from the hand-crafted treebank information. 1 1 Introduction Subcategoris...