tech,9-1-H01-1049,bq paradigm for <term> human interaction with data sources </term> . We integrate a <term> spoken
lr,8-6-N01-1003,bq automatically learned from <term> training data </term> . We show that the trained <term> SPR
lr,19-2-P01-1004,bq both <term> character - and word-segmented data </term> , in combination with a range of <term>
other,34-2-P01-1047,bq learning algorithm </term> from <term> structured data </term> ( based on a <term> typing-algorithm
lr,15-1-N03-1001,bq manual transcription </term> of <term> training data </term> . The method combines <term> domain
lr,7-4-N03-1012,bq <term> system </term> against the <term> annotated data </term> shows that , it successfully classifies
lr,2-1-N03-2003,bq </term> result . Sources of <term> training data </term> suitable for <term> language modeling
other,27-1-N03-4004,bq inflow of <term> multilingual , multimedia data </term> . It gives users the ability to spend
other,15-3-N03-4010,bq browsing the <term> repository </term> of <term> data objects </term> created by the <term> system
other,13-1-P03-1005,bq </term> for <term> structured natural language data </term> . The <term> HDAG Kernel </term> directly
other,15-1-P03-1009,bq classes </term> from undisambiguated <term> corpus data </term> . We describe a new approach which
lr,13-4-P03-1033,bq learning </term> using real <term> dialogue data </term> collected by the <term> system </term>
lr,14-1-P03-1058,bq is the lack of <term> manually sense-tagged data </term> required for <term> supervised learning
other,6-3-P03-1068,bq experiences and evaluate the <term> annotated data </term> from the first project stage . On
lr-prod,6-4-C04-1112,bq </term> on the <term> Dutch SENSEVAL-2 test data </term> , we achieve a significant increase
other,8-1-N04-4028,bq structured databases </term> from <term> unstructured data sources </term> , such as the <term> Web </term>
lr,13-1-H05-1012,bq </term> based on <term> supervised training data </term> . We demonstrate that it is feasible
lr,17-5-H05-1095,bq better generalize from the <term> training data </term> . This paper investigates some <term>
lr,9-1-H05-2007,bq <term> patterns </term> in <term> translation data </term> using <term> part-of-speech tag sequences
other,23-9-J05-1003,bq feature space </term> in the <term> parsing data </term> . Experiments show significant efficiency
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