The DOP approach of using all subtrees from an annotated corpus for
statistical parsing has recently gained renewed interest (e.g. Collins &
Duffy 2002; Johnson 2002; Neumann 2003; Joshi & Sarkar 2003). While the
computational complexity of using all subtrees deterred its wider use for
some time, several new algorithms have recently been developed that allow
for fast processing. In this presentation, I will give an overview of
Data-Oriented Parsing and go into the latest developments, among which
HPSG-DOP, TAG-DOP and Data-Oriented Translation. I will also generalize
DOP to "Data-Oriented Perception" and discuss some experiments with
musical corpora, suggesting an interesting parallel between linguistic and
musical processing.