Paper: Memory-Based One-Step Named-Entity Recognition: Effects Of Seed List Features Classifier Stacking And Unannotated Data

ACL ID W03-0427
Title Memory-Based One-Step Named-Entity Recognition: Effects Of Seed List Features Classifier Stacking And Unannotated Data
Venue International Conference on Computational Natural Language Learning
Session Main Conference
Year 2003
Authors

on over them). New (test) instances are classified by matching them to all instances in memory, and by calculating with each match the distance, given by a distance function between the new instance X and each of the n memory instances Y1...n. Classification in memory-based learning is per- formed by the k-NN algorithm that searches for the k ‘nearest neighbours’ among the memory instances ac- cording to the distance function. The majority class of the k nearest neighbours then determines the class of the new instance X. Cf. (Daelemans et al. , 2002) for algo- rithmic details and background. 3.2 Iterative deepening Iterative deepening (ID) is a heuristic search algorithm for the optimization of algorithmic parameter and fea- ture selection, that combines classifier wrapping (using the ...