</term> from <term> structured data </term> ( based on a <term> typing-algorithm </term> and <term>
approach to question answering </term> which is based on combining the results from different
implementation of the <term> model </term> based on <term> finite-state models </term> , demonstrate
translation quality </term> of <term> EBMT </term> based on a small-sized <term> bilingual corpus </term>
on <term> block selection criteria </term> based on <term> unigram </term> counts and <term> phrase
<term> dialogue system </term> . We build this based on both <term> Finite State Model ( FSM )
central to our <term> IE paradigm </term> . It is based on : ( 1 ) an extended set of <term> features
tech,3-1-P03-1022,bq data </term> . We apply a <term> decision tree based approach </term> to <term> pronoun resolution
to understand <term> user utterances </term> based on the <term> context </term> of a <term> dialogue
for resolving this <term> ambiguity </term> based on <term> statistical information </term> obtained
dimensions . <term> Dialogue strategies </term> based on the <term> user modeling </term> are implemented
</term> . The <term> stemming model </term> is based on <term> statistical machine translation
of lack of <term> negative feedback </term> . Based on these results , we present an <term> ECA
built a <term> generation algorithm </term> based on the results . The evaluation using another
tech,3-5-C04-1112,bq model </term> . Also , the <term> WSD system based on lemmas </term> is smaller and more robust
finding <term> synonymous expressions </term> based on the <term> distributional hypothesis </term>
coherent <term> corpus </term> . Our approach is based on the idea that one person tends to use
We show that various <term> features </term> based on the structure of <term> email-threads </term>
for <term> word sense disambiguation </term> based on <term> parallel corpora </term> . The method
alignment </term> and <term> word clustering </term> based on <term> automatic extraction of translation
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