lr,19-2-P01-1004,bq both <term> character - and word-segmented data </term> , in combination with a range of <term>
lr-prod,6-4-C04-1112,bq </term> on the <term> Dutch SENSEVAL-2 test data </term> , we achieve a significant increase
other,23-9-J05-1003,bq feature space </term> in the <term> parsing data </term> . Experiments show significant efficiency
other,13-1-P05-1067,bq statistical models </term> to <term> structured data </term> . In this paper , we present a <term>
other,27-1-N03-4004,bq inflow of <term> multilingual , multimedia data </term> . It gives users the ability to spend
other,15-1-C86-1132,bq forecasts directly from <term> formatted weather data </term> . Such <term> synthesis </term> appears
other,13-1-P03-1005,bq </term> for <term> structured natural language data </term> . The <term> HDAG Kernel </term> directly
lr,15-1-N03-1001,bq manual transcription </term> of <term> training data </term> . The method combines <term> domain
lr,17-5-H05-1095,bq better generalize from the <term> training data </term> . This paper investigates some <term>
other,30-4-P03-1009,bq classifying </term><term> undisambiguated SCF data </term> . We apply a <term> decision tree based
lr,13-1-H05-1012,bq </term> based on <term> supervised training data </term> . We demonstrate that it is feasible
other,15-1-P03-1009,bq classes </term> from undisambiguated <term> corpus data </term> . We describe a new approach which
lr,8-6-N01-1003,bq automatically learned from <term> training data </term> . We show that the trained <term> SPR
other,34-2-P01-1047,bq learning algorithm </term> from <term> structured data </term> ( based on a <term> typing-algorithm
other,5-2-C92-1055,bq the problem of <term> insufficient training data </term> and <term> approximation error </term>
measure(ment),3-4-J05-4003,bq evaluate the <term> quality of the extracted data </term> by showing that it improves the performance
lr,43-2-N03-2003,bq bigger performance gains from the <term> data </term> by using <term> class-dependent interpolation
lr,7-2-N03-2003,bq In this paper , we show how <term> training data </term> can be supplemented with <term> text
lr,13-4-P03-1033,bq learning </term> using real <term> dialogue data </term> collected by the <term> system </term>
Information System ) domain </term> . This data collection effort has been co-ordinated
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