Paper: Memory-Based Semantic Role Labeling: Optimizing Features Algorithm And Output

ACL ID W04-2414
Title Memory-Based Semantic Role Labeling: Optimizing Features Algorithm And Output
Venue International Conference on Computational Natural Language Learning
Session Main Conference
Year 2004
Authors

From these “nearest neighbors”, the class of the test item is extrapolated. See Daelemans et al. (2003) for a detailed description of the algorithms and metrics used in our experiments. All memory-based learning experiments were done with the TiMBL software package1. In previous research, we have found that memorybased learning is rather sensitive to the chosen features and the particular setting of its algorithmic parameters (e.g. the number of nearest neighbors taken into account, the function for extrapolation from the nearest neighbors, the feature relevance weighting method used, etc.). In order to minimize the effects of this sensitivity, we have put much effort in trying to find the best set of features and the optimal learner parameters for this particular task. 3.2 Feature sel...