In this paper we present first results from a comparative study. Its aim is to test the feasibility of different inductive learning techniques to perform the automatic acquisition of linguistic knowledge within a natural language database interface. In our interface architecture the machine learning module replaces an elaborate semantic analysis component. The learning module learns the correct mapping of a user's input to the corresponding database command based on a collection of past input data. We use an existing interface to a production planning and control system as evaluation and compare the results achieved by different instance-based and model-based learning algorithms.
@article{arxiv.cmp-lg/9705012,
title = {A Comparative Study of the Application of Different Learning Techniques to Natural Language Interfaces},
author = {Werner Winiwarter and Yahiko Kambayashi},
journal= {arXiv preprint arXiv:cmp-lg/9705012},
year = {2008}
}