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 Fβ=1for 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.
CITATION STYLE
Daelemans, W., Buchholz, S., & Veenstra, J. (1999). Memory-Based Shallow Parsing. In CoNLL 1999 - Computational Natural Language Learning, Held in cooperation with 9th Conference of the European Chapter of the Association for Computational Linguistics, EACL 1999 - Proceedings of a Workshop (pp. 53–60). Association for Computational Linguistics (ACL).
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