cm p - lg / 9 60 60 22 17 J un 1 99 6 Two Questions about Data - Oriented Parsing

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 still 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. 1 Introduction The Data-Oriented Parsing (DOP) method suggested in Scha (1990) and developed in Bod (1992-1995) is a probabilistic parsing strategy which does not single out a narrowly predefined set of structures as the statistically significant ones. It accomplishes this by maintaining a large corpus of analyses of previously occurring utterances. New input is parsed by combining tree-fragments from the corpus; the frequencies of these fragments are used to estimate which analysis is the most probable one. In previous work, we tested the DOP method on a cleaned-up set of analyzed part-of-speech strings from the Penn Treebank (Marcus et al., 1993), achieving excellent test results (Bod, 1993a,b). 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. The rest of it is divided into three parts. In section 2 we give a short resume of the DOP method. In section 3 we address the first question: how does DOP perform on unedited data? In section 4 we deal with the question how DOP can be used for parsing word strings that contain unknown words. This second question turns out to be the actual focus of the article, while the answer …