The goal of this chapter is to show that even complex recursive NLP tasks such as parsing (assigning syntactic structure to sentences using a grammar, a lexicon and a search algorithm) can be redefined as a set of cascaded classification problems with separate classifiers for tagging, chunk boundary detection, chunk labeling, relation finding, etc. In such an approach, input vectors represent a focus item and its surrounding context, and output classes represent either a label of the focus (e.g., part of speech tag, constituent label, type of grammatical relation) or a segmentation label (e.g., start or end of a constituent). In this chapter, we show how a shallow parser can be constructed as a cascade of MBLP-classifiers and introduce software that can be used for the development of memory-based taggers and chunkers.

Although in principle full parsing could be achieved in this modular, classification-based way (see section 5.5), this approach is more suited for shallow parsing. Partial or shallow parsing, as opposed to full parsing, recovers only a limited amount of syntactic information from natural language sentences. Especially in applications such as information retrieval, question answering, and information extraction, where large volumes of, often ungrammatical, text have to be analyzed in an efficient and robust way, shallow parsing is useful. For these applications a complete syntactic analysis may provide too much or too little information.