ubiquitous and carries important information yet it is also time consuming to document . Given
the <term> annotated data </term> shows that , it successfully classifies 73.2 % in a <term>
<term> OCR systems </term> in order to make it more useful for <term> NLP tasks </term> .
<term> recognition task </term> , but also that it is possible to get bigger performance gains
create a <term> word-trie </term> , transform it into a <term> minimal DFA </term> , then identify
as the <term> cohesion constraint </term> . It requires disjoint <term> English phrases </term>
algorithms </term> . The results show that it can provide a significant improvement in
<term> multilingual , multimedia data </term> . It gives users the ability to spend their
central to our <term> IE paradigm </term> . It is based on : ( 1 ) an extended set of <term>
Switchboard dialogues </term> and show that it compares well to <term> Byron 's ( 2002 )
</term> of <term> speech understanding </term> , it is not appropriate to decide on a single
statistical machine translation </term> and it uses an <term> English stemmer </term> and
improve the <term> stemmer </term> by allowing it to adapt to a desired <term> domain </term>
manually segmented Arabic corpus </term> and uses it to bootstrap an <term> unsupervised algorithm
<term> word </term> to be tagged , and evaluate it in both the <term> unsupervised and supervised
</term> among the <term> sentences </term> that it contains . We give two estimates , a lower
The advantage of this novel method is that it clusters all <term> inflected forms </term>
allowing fast adaptation to applications and it is scalable . We apply it in combination
applications and it is scalable . We apply it in combination with a <term> terabyte corpus
always be imperfect . For many reasons , it is highly desirable to accurately estimate
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