an <term> expert human translation </term> or a <term> machine translation </term> . Additionally
their logistics system to place a supply or information request . The request is passed
the status of a <term> request </term> changes or when a <term> request </term> is complete .
measure(ment),19-3-H01-1058,bq performance </term> ( typically , <term> word or semantic error rate </term> ) from a list
</term> using a <term> neural network </term> or a <term> decision tree </term> . The method
decision of how to combine them into one or more <term> sentences </term> . In this paper
language </term> , current systems use manual or semi-automatic methods to collect <term>
tech,32-1-P01-1056,bq compete with <term> hand-crafted template-based or rule-based approaches </term> . In this paper
2.284 <term> SRHs </term> as either coherent or incoherent ( given a <term> baseline </term>
only requires a few <term> common noun </term> or <term> pronoun </term><term> seeds </term> that
<term> user </term> 's <term> knowledge </term> or typical kinds of <term> users </term> , the
to adapt to a desired <term> domain </term> or <term> genre </term> . Examples and results
* - stem-suffix * </term> ( * denotes zero or more occurrences of a <term> morpheme </term>
been comparable across <term> corpora </term> or <term> lexicons </term> . Observing that the
similarity </term> between <term> words </term> or use <term> lexical affinity </term> to create
patterns </term> of any pair of <term> words </term> or <term> phrases </term> at any distance in the
sources </term> , such as the <term> Web </term> or <term> newswire documents </term> . Despite
Given a particular <term> concept </term> , or <term> word sense </term> , a <term> topic signature
systems </term> , such as <term> BLEU </term> or <term> NIST </term> , are now well established
pairs </term> like <term> English-Chinese </term> or <term> English-Japanese </term> , because of
hide detail