lr,11-2-P03-1058,bq automatically acquire <term> sense-tagged training data </term> from <term> English-Chinese parallel
other,16-2-P05-1032,bq translations </term> in our <term> suffix array-based data structure </term> . We show how <term> sampling
lr,2-1-N03-2003,bq </term> result . Sources of <term> training data </term> suitable for <term> language modeling
lr,6-3-J05-4003,bq approach </term> , we extract <term> parallel data </term> from large <term> Chinese , Arabic
other,13-1-P05-1067,bq statistical models </term> to <term> structured data </term> . In this paper , we present a <term>
other,7-1-P05-1032,bq In this paper we describe a novel <term> data structure </term> for <term> phrase-based statistical
lr,7-4-N03-1012,bq <term> system </term> against the <term> annotated data </term> shows that , it successfully classifies
other,8-1-N04-4028,bq structured databases </term> from <term> unstructured data sources </term> , such as the <term> Web </term>
other,15-1-P03-1009,bq classes </term> from undisambiguated <term> corpus data </term> . We describe a new approach which
other,30-4-P03-1009,bq classifying </term><term> undisambiguated SCF data </term> . We apply a <term> decision tree based
other,5-2-C92-1055,bq the problem of <term> insufficient training data </term> and <term> approximation error </term>
tech,9-1-H01-1049,bq paradigm for <term> human interaction with data sources </term> . We integrate a <term> spoken
lr,9-1-H05-2007,bq <term> patterns </term> in <term> translation data </term> using <term> part-of-speech tag sequences
Information System ) domain </term> . This data collection effort has been co-ordinated
ability to spend their time finding more data relevant to their task , and gives them
other,13-1-P03-1005,bq </term> for <term> structured natural language data </term> . The <term> HDAG Kernel </term> directly
other,15-1-C86-1132,bq forecasts directly from <term> formatted weather data </term> . Such <term> synthesis </term> appears
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
lr,15-1-N03-1001,bq manual transcription </term> of <term> training data </term> . The method combines <term> domain
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