Memory-based re-engineering of a knowledge-based dependency parser

The emulation of a knowledge-based dependency parser for Dutch by a fast approximation of a memory-based learning algorithm is described. During the development of the original parser, hand-parsed test sentences were collected to offer stochastic guidance in the the parsing process. Training a memory-based parser directly on these collections yields a reasonable but not very accurate emulation. However, when we train the memory-based parser on a much larger collection of texts that were automatically parsed by the knowledge-based parser, it is possible to prolong the learning curve. The resulting re-engineered parser performs at linear speed in function of the length of the input sequence; through brute force, the costly computations of the parser are precompiled into memory, from which retrieval is cheap.