lr,7-4-N03-1012,bq <term> system </term> against the <term> annotated data </term> shows that , it successfully classifies
other,6-3-P03-1068,bq experiences and evaluate the <term> annotated data </term> from the first project stage . On
other,16-2-P05-1032,bq translations </term> in our <term> suffix array-based data structure </term> . We show how <term> sampling
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>
measure(ment),3-4-J05-4003,bq evaluate the <term> quality of the extracted data </term> by showing that it improves the performance
other,13-1-P03-1005,bq </term> for <term> structured natural language data </term> . The <term> HDAG Kernel </term> directly
ability to spend their time finding more data relevant to their task , and gives them
other,27-1-N03-4004,bq inflow of <term> multilingual , multimedia data </term> . It gives users the ability to spend
tech,15-3-H92-1003,bq the implementation of a <term> multi-site data collection paradigm </term> , and the accomplishments
other,7-1-P05-1032,bq In this paper we describe a novel <term> data structure </term> for <term> phrase-based statistical
other,15-3-N03-4010,bq browsing the <term> repository </term> of <term> data objects </term> created by the <term> system
</term> to recover a <term> submanifold </term> of data from a <term> high dimensionality space </term>
lr,6-3-J05-4003,bq approach </term> , we extract <term> parallel data </term> from large <term> Chinese , Arabic
other,23-9-J05-1003,bq feature space </term> in the <term> parsing data </term> . Experiments show significant efficiency
other,30-4-P03-1009,bq classifying </term><term> undisambiguated SCF data </term> . We apply a <term> decision tree based
tech,5-3-P03-1058,bq this <term> method of acquiring sense-tagged data </term> is promising . On a subset of the
lr,14-1-P03-1058,bq is the lack of <term> manually sense-tagged data </term> required for <term> supervised learning
lr,37-4-P03-1058,bq advantage that <term> manually sense-tagged data </term> have in their <term> sense coverage
lr-prod,5-5-P06-1013,bq the <term> SemCor </term> and <term> Senseval-3 data sets </term> demonstrate that our ensembles
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