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

dc.creatorWiniwarter, Werner
dc.creatorKambayashi, Yahiko
dc.date1997-05-16
dc.date.accessioned2026-07-07T09:10:48Z
dc.date.available2026-07-07T09:10:48Z
dc.descriptionIn 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.
dc.description10 pages, to appear CoNLL97
dc.identifierhttps://arxiv.org/abs/cmp-lg/9705012
dc.identifierhttp://arxiv.org/abs/cmp-lg/9705012
dc.identifier.urihttp://salesiana.dossiersoluciones.com/handle/123456789/151462
dc.subjectComputation and Language
dc.titleA Comparative Study of the Application of Different Learning Techniques to Natural Language Interfaces
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