in less than 100 <term> words </term> . Even more illuminating was the factors on which the
as <term> dialog systems </term> understand more of what the <term> user </term> tells them
<term> user </term> tells them , they need to be more sophisticated at responding to the <term>
</term> . Our <term> algorithm </term> reported more than 99 % <term> accuracy </term> in both <term>
decision of how to combine them into one or more <term> sentences </term> . In this paper ,
<term> OCR systems </term> in order to make it more useful for <term> NLP tasks </term> . We present
achieving similar <term> performance </term> to more complex mixtures of techniques . We present
<term> Statistical approach </term> is much more robust but less accurate . <term> Cooperative
the ability to spend their time finding more data relevant to their task , and gives
the <term> user model </term> we propose is more comprehensive . Specifically , we set up
stem-suffix * </term> ( * denotes zero or more occurrences of a <term> morpheme </term> )
approximation of <term> HPSG </term> produces a more effective <term> CFG filter </term> than that
system based on lemmas </term> is smaller and more robust . We present a <term> text mining
machine translation systems </term> provides yet more <term> redundancy </term> , yielding different
bilingual parallel corpora </term> , a much more commonly available <term> resource </term>
computations involving the higher ( and more useful ) <term> models </term> are <term> hard
results can be improved using a bigger and a more homogeneous <term> corpus </term> to train
</term> . <term> Path-based inference </term> is more efficient , while <term> node-based inference
while <term> node-based inference </term> is more general . A method is described of combining
techniques and the still valuable methods of more traditional <term> natural language interfaces
hide detail