browsers </term> . At MIT Lincoln Laboratory , we have been developing a <term> Korean-to-English
automated acquisition of grammars </term> . Having been trained on <term> Korean newspaper articles
</term> and <term> information sources </term> . We have built and will demonstrate an application
when a <term> request </term> is complete . We have demonstrated this capability in several
Automatic Speech Recognition technology </term> have put the goal of naturally sounding <term>
improved <term> speech recognition </term> has brought to light a new problem : as <term>
system response </term> to <term> users </term> has been extensively studied by the <term> natural
</term> , where each <term> word string </term> has been obtained by using a different <term>
concatenation grammar [ RCG ] formalism </term> has revealed many attractive properties which
a <term> natural language generator </term> have recently been proposed , but a fundamental
word-level alignment models </term> does not have a strong impact on performance . Learning
for <term> language understanding </term> and have a high accuracy but little robustness and
shortcomings of different strategies and has all the advantages of the three strategies
baseline methods </term> . Previous research has demonstrated the utility of <term> clustering
system </term> . <term> Link detection </term> has been regarded as a core technology for
understanding process </term> . Experiment results have shown that a <term> system </term> that exploits
Kyoto city bus information system </term> that has been developed at our laboratory . Experimental
that <term> manually sense-tagged data </term> have in their <term> sense coverage </term> . Our
formulate our <term> heuristic principles </term> have significant <term> predictive power </term>
that our <term> filtering mechanism </term> has a significant positive effect on both tasks
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