Memory-Based Shallow Parsing
Walter M. P. Daelemans, Sabine Buchholz, Jorn Veenstra · Research portal (Tilburg University) · 1999
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 F-value 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.