A Comparative Study of the Application of Different Learning Techniques to Natural Language Interfaces
Abstract
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.
- Publication:
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arXiv e-prints
- Pub Date:
- May 1997
- DOI:
- 10.48550/arXiv.cmp-lg/9705012
- arXiv:
- arXiv:cmp-lg/9705012
- Bibcode:
- 1997cmp.lg....5012W
- Keywords:
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- Computer Science - Computation and Language
- E-Print:
- 10 pages, to appear CoNLL97