Walter Daelemans, walter@kub.nl
Sabine Buchholz, buchholz@kub.nl
Jorn Veenstra, veenstra@kub.nl
We present a memory-based learning (MBL) approach to shallow parsing in which POS tagging, chunking, and identification of syntactic relations are formulated as memory-based modules. The experiments reported in this paper show competitive results, the Fß=1 for the Wall Street Journal (WSJ) treebank is: 93.8% for NP chunking, 94.7% for VP chunking, 77.1% for subject detection and 79.0% for object detection.
Relevant url: http://ilk.kub.nl/cgi-bin/chunkdemo/demo.pl
Postscript supplied by author: http://ilk.kub.nl/downloads/pub/papers/ilk.9907.ps.gz