Lazy Learning Algorithms for Problems with Many Binary Features and Classes
We have designed several new lazy learning algorithms for learning problems with many binary features and classes. This particular type of learning task can be found in many machine learning applications but is of special importance for machine learning of natural language. Besides pure instance-based learning we also consider prototype-based learning, which has the big advantage of a large reduction of the required memory and processing time for classification. As an application for our learning algorithms we have chosen natural language database interfaces. In our interface architecture the machine learning module replaces an elaborate semantic analysis component. The learning task is to select the correct command class based on semantic features extracted from the user input. We use an existing German natural language interface to a production planning and control system as a case study for our evaluation and compare the results achieved by the different lazy learning algorithms.
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- 1.Aha, D. W., Kibler, D., Albert, M.: Instance-based learning algorithms. Machine Learning 7 (1991) 37–66 113Google Scholar
- 3.Barja, M. L. et al.: An effective deductive object-oriented database through language integration. Proc. of the Intl. Conf. on Very Large Data Bases (1994) 463–474 113Google Scholar
- 4.Daelemans, W., van den Bosch, A.: Generalisation performance of backpropagation learning on a syllabification task. Drossaers, M., Nijholt, A. (eds): TWLT3: Connectionism and Natural Language Processing. Twente University Press, Enschede (1992) 27–37 113Google Scholar
- 5.Datta, P., Kibler, D.: Learning prototypical concept descriptions. Proc. of the Intl. Conf. on Machine Learning (1995) 41–45 113Google Scholar
- 7.Quinlan, J. R.: Induction of decision trees. Machine Learning 1 (1986) 81–206 113Google Scholar
- 10.Uehara, K., Tanizawa, M., Maekawa, S.: PBL: Prototype-based learning algorithm. 113 Wess, S., Althoff, K.-D., Richter, M. M. (eds): Topics in Case-Based Reasoning. Springer-Verlag, Berlin (1994)Google Scholar
- 11.Winiwarter, W.: The Integrated Deductive Approach to Natural Language Interfaces. Ph.D. thesis, University of Vienna (1994) 117Google Scholar
- 12.Winiwarter, W.: MIDAS — the morphological component of the IDA system for efficient natural language interface design. Proc. of the Intl. Conf. on Database and Expert Systems Applications (1995) 584–593 115Google Scholar
- 13.Winiwarter, W.: Unknown value lists and their use for semantic analysis in IDA — the integrated deductive approach to natural language interface design. Proc. of the Australasian Database Conf. (1996) 194–203 115Google Scholar
- 14.Winiwarter, W., Kambayashi, Y.: A comparative study of the application of different learning techniques to natural language interfaces. Proc. of the Workshop on Computational Natural Language Learning (1997) 125–135 116Google Scholar
- 15.Winiwarter, W., Kambayashi, Y.: A machine learning workbench in a DOOD framework. Proc. of the Intl. Conf. on Database and Expert Systems Applications (1997) 452–461 113Google Scholar
- 16.Zavrel, J., Daelemans, W.: Memory-based learning: Using similarity for smoothing. Proc. of the Annual Meeting of the ACL and Conf. of the European Chapter of the ACL (1997) 436–443 112Google Scholar