Reducing the Size of the Representation for the uDOP-Estimate

The unsupervised Data Oriented Parsing (uDOP) approach has been repeatedly reported to achieve state of the art performance in experiments on parsing of different corpora. At the same time the approach is demanding both in computation time and memory. This paper describes an approach which decreases these demands. First the problem is translated into the generation of probabilistic bottom up tree automata (pBTA). Then it is explained how solving two standard problems for these automata results in a reduction in the size of the grammar. The reduction of the grammar size by using efficient algorithms for pBTAs is the main contribution of this paper. Experiments suggest that this leads to a reduction in grammar size by a factor of 2. This paper also suggests some extensions of the original uDOP algorithm that are made possible or aided by the use of tree automata.

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