Memory-Based Shallow Parsing
Erik F. Tjong Kim Sang;
2(Mar):559-594, 2002.
Abstract
We present memory-based learning approaches to shallow parsing
and apply these to five tasks: base noun phrase identification,
arbitrary base phrase recognition, clause detection, noun phrase
parsing and full parsing. We use feature selection techniques and
system combination methods for improving the performance of the
memory-based learner. Our approach is evaluated on standard data
sets and the results are compared with that of other systems.
This reveals that our approach works well for base phrase
identification while its application towards recognizing embedded
structures leaves some room for improvement.
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