this paper we show how two standard outputs from <term> information extraction ( IE ) systems
other,9-4-H01-1042,bq assessors </term> can differentiate <term> native from non-native language essays </term> in less
see if similar criteria could be elicited from duplicating the experiment using <term> machine
<term> word or semantic error rate </term> ) from a list of <term> word strings </term> , where
ranking rules </term> automatically learned from <term> training data </term> . We show that
<term> identification of paraphrases </term> from a <term> corpus of multiple English translations
</term> , and a <term> learning algorithm </term> from <term> structured data </term> ( based on a
These <term> models </term> , which are built from <term> shallow linguistic features </term>
</term> which is based on combining the results from different <term> answering agents </term> searching
resolution algorithm </term> that combines results from the <term> answering agents </term> at the <term>
</term> of <term> phrase translations </term> from <term> word-based alignments </term> and <term>
words </term> and learning <term> phrases </term> from <term> high-accuracy word-level alignment
noisy channel framework </term> , progressing from generation of <term> true text </term> through
</term> of <term> translation lexicons </term> from <term> printed text </term> . We present an
can be supplemented with <term> text </term> from the <term> web </term> filtered to match the
possible to get bigger performance gains from the <term> data </term> by using <term> class-dependent
</term> , the <term> blocks </term> are learned from <term> source interval projections </term>
topical report </term> , culling information from a large inflow of <term> multilingual , multimedia
most likely <term> answer candidates </term> from the given <term> text corpus </term> . The
inducing <term> semantic verb classes </term> from undisambiguated <term> corpus data </term>
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