results are presented , that demonstrate how the proposed <term> method </term> allows to
context . We identified two tasks : First , how <term> linguistic concepts </term> are acquired
different <term> inference types </term> , and how the information found in <term> memory </term>
pragmatics processing </term> , we describe how the method of <term> abductive inference </term>
based on processing . Finally , it shows how processing accounts can be described formally
<term> monolingual UCG </term> , we will show how the two can be integrated , and present
time </term> . Furthermore , we will show how some <term> evaluation measures </term> can
that <term> users </term> need by analyzing how a <term> user </term> interacts with a system
<term> features </term> , without concerns about how these <term> features </term> interact or overlap
</term> . The demonstration will focus on how <term> JAVELIN </term> processes <term> questions
restrictive statements </term> . The paper shows how conventional algorithms for the analysis
translation probabilities </term> , and show how it can be refined to take <term> contextual
characterization of what a <term> user model </term> is and how it can be used . The types of information
inference types </term> . The paper also discusses how <term> memory </term> is structured in multiple
</term> , the <term> theory </term> specifies how different information in <term> memory </term>
translation systems </term> , and demonstrate how our application can be used by <term> developers
particular , we here elaborate on principles of how the <term> global behavior </term> of a <term>
for this purpose . In this paper we show how two standard outputs from <term> information
statistical machine translation </term> , we show how <term> paraphrases </term> in one <term> language
Discourse processing </term> requires recognizing how the <term> utterances </term> of the <term> discourse
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