[Tjong Kim Sang(2002)] considered the issues involved with applying memory-based learning (MBL) to shallow parsing. MBL consistently performs well for a variety of shallow parsing tasks, often yielding (near) best results Daelemans+99,Buchholz+99. From this, one might conclude that MBL was a promising learning technique for pushing shallow parsing to full parsing. For full parsing, MBL fared less well, however, and the results were not as good as for the other parsers that were compared. This does not mean that MBL is fundamentally unsuited for full-blown parsing. Instead, it suggests that the task needs to be encoded in some other manner.
In his paper, a weakness of MBL | that it can have difficulty handling large numbers of features | was identified. A feature selection method, namely bidirectional hill climbing caruana94, was found to yield insignificant gains in performance for NP parsing. However, it did produce a significant improvement for clause identification.
Tjong Kim Sang also showed how ensemble learning techniques such as (weighted) majority voting and stacking could improve upon performance. All system combination methods improved on the results of the individual MBL classifiers, and the best performer was to employ MBL itself as a stacked classifier.