A Comparative Study of the Application of Different Learning Techniques to Natural Language Interfaces

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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.
10 pages, to appear CoNLL97

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Computation and Language

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http://salesiana.dossiersoluciones.com/handle/123456789/151462

Consulte el texto completo en el siguiente enlace:

https://arxiv.org/abs/cmp-lg/9705012
 http://arxiv.org/abs/cmp-lg/9705012

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arXiv