generation of <term> true text </term> through its transformation into the <term> noisy output
sentence alignment tasks </term> to evaluate its performance as a <term> similarity measure
sentences ) <term> parallel corpus </term> as its sole <term> training resources </term> . No
distributional word feature vectors </term> and its impact on <term> word similarity </term> results
information extraction tasks </term> because of its ability to capture arbitrary , overlapping
BLEU </term> in <term> word n-grams </term> and its application at the <term> character </term>
applied to these diverse problems , discuss its application , and evaluate its performance
, discuss its application , and evaluate its performance . State-of-the-art <term> Question
<term> text mining </term> . As evidence of its usefulness and usability , it has been
and tables indispensable to our papers . Its selection of <term> fonts </term> , specification
directions . Each <term> character </term> has its own width and a line length is counted
accept <term> natural language input </term> from its <term> users </term> on a routine basis , it
is presented along with a description of its <term> implementation </term> in a <term> voice
Unification </term> is attractive , because of its generality , but it is often computationally
the beginning of the sentence ) extending its activity usually in a rightward manner
program <term> RINA </term> , which enhances its own <term> lexical hierarchy </term> by processing
different amounts and types of information into its <term> lexicon </term> according to its individual
into its <term> lexicon </term> according to its individual needs . However , some of the
some computational issues that arise in its interpretation . This paper proposes that
efficiency through a <term> reduction </term> of its <term> search space </term> . As each new <term>
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