  In this paper I present ongoing work on the data-oriented parsing (DOP)
model. In previous work, DOP was tested on a cleaned-up set of analyzed
part-of-speech strings from the Penn Treebank, achieving excellent test
results. This left, however, two important questions unanswered: (1) how does
DOP perform if tested on unedited data, and (2) how can DOP be used for parsing
word strings that contain unknown words? This paper addresses these questions.
We show that parse results on unedited data are worse than on cleaned-up data,
although very competitive if compared to other models. As to the parsing of
word strings, we show that the hardness of the problem does not so much depend
on unknown words, but on previously unseen lexical categories of known words.
We give a novel method for parsing these words by estimating the probabilities
of unknown subtrees. The method is of general interest since it shows that good
performance can be obtained without the use of a part-of-speech tagger. To the
best of our knowledge, our method outperforms other statistical parsers tested
on Penn Treebank word strings.
