A Cascaded Machine Learning Approach to Interpreting Temporal Expressions
David Ahn, Joris van Rantwijk, Maarten de Rijke · UvA-DARE (University of Amsterdam) · 2007
A new architecture for identifying and interpreting temporal expressions is introduced, in which the large set of complex hand-crafted rules standard in systems for this task is replaced by a series of machine learned classifiers and a much smaller set of context-independent semantic composition rules. Experiments with the TERN 2004 data set demonstrate that overall system performance is comparable to the state-of-the-art, and that normalization performance is particularly good. 1