Memory-Based Shallow Parsing

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.

