Fast NP Chunking Using Memory-Based Learning Techniques (1998)
| Venue: | In Proceedings of BENELEARN'98 |
| Citations: | 26 - 1 self |
BibTeX
@INPROCEEDINGS{Veenstra98fastnp,
author = {Jorn Veenstra and Sabine Buchholz},
title = {Fast NP Chunking Using Memory-Based Learning Techniques},
booktitle = {In Proceedings of BENELEARN'98},
year = {1998},
pages = {71--78}
}
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Abstract
In this paper we discuss the application of Memory-Based Learning (MBL) to fast NP chunking. We first discuss the application of a fast decision tree variant of MBL (IGTree) on the dataset described in (Ramshaw and Marcus, 1995), which consists of roughly 50,000 test and 200,000 train items. In a second series of experiments we used an architecture of two cascaded IGTrees. In the second level of this cascaded classifier we added context predictions as extra features so that incorrect predictions from the first level can be corrected, yielding a 97.2% generalisation accuracy with training and testing times in the order of seconds to minutes. Submission Type: regular paper Topic Areas: robust parsing, NP chunking, memory-based learning Author of Record: Jorn Veenstra Under consideration for other conferences (specify)? no Fast NP Chunking Using Memory-Based Learning Techniques Abstract In this paper we discuss the application of Memory-Based Learning (MBL) to fast NP chunking. We fir...
