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</term> is ubiquitous and carries important	information	yet it is also time consuming to document	#7 Oral communication is ubiquitous and carries important information yet it is also time consuming to document.
question is , however , how an interesting	information	piece would be found in a <term> large database	#45 The question is, however, how an interesting information piece would be found in a large database.
text browser </term> . We describe how this	information	is used in a <term> prototype system </term>	#317 We describe how this information is used in a prototype system designed to support information workers' access to a pharmaceutical news archive as part of their industry watch function.
evaluation techniques </term> will provide	information	about both the <term> human language learning	#589 We believe that these evaluation techniques will provide information about both the human language learning process, the translation process and the development of machine translation systems.
their logistics system to place a supply or	information	request . The request is passed to a <term>	#844 Using LCS-Marine, tactical personnel can converse with their logistics system to place a supply or information request.
write a <term> topical report </term> , culling	information	from a large inflow of <term> multilingual	#3593 The TAP-XL Automated Analyst's Assistant is an application designed to help an English-speaking analyst write a topical report, culling information from a large inflow of multilingual, multimedia data.
</term> is in <term> English </term> . Typically ,	information	that makes it to a <term> summary </term> appears	#7178 Typically, information that makes it to a summary appears in many different lexical-syntactic forms in the input documents.
involves manual determination of whether an	information	nugget appears in a system 's response	#7561 Until now, the only way to assess the correctness of answers to such questions involves manual determination of whether an information nugget appears in a system's response.
features </term> . Then , we explore whether	information	about <term> meeting context </term> can aid	#10283 Then, we explore whether information about meeting context can aid classifiers' performances.
classifiers </term> show little <term> gain </term> from	information	about <term> meeting context </term> . Most	#10318 The classifiers show little gain from information about meeting context.
</term> interacts with a system while gathering	information	related to a particular scenario . This	#11689 FERRET utilizes a novel approach to Q/A known as predictive questioning which attempts to identify the questions (and answers) that users need by analyzing how a user interacts with a system while gathering information related to a particular scenario.
<term> theory </term> specifies how different	information	in <term> memory </term> affects the certainty	#11953 Unlike logic, the theory specifies how different information in memory affects the certainty of the conclusions drawn.
<term> inference types </term> , and how the	information	found in <term> memory </term> determines which	#12039 The paper also discusses how memory is structured in multiple ways to support the different inference types, and how the information found in memory determines which inference types are triggered.
<term> recognition tasks </term> the role of	information	from the <term> discourse </term> and from	#14379 This processing description specifies in these recognition tasks the role of information from the discourse and from the participants' knowledge of the domain.
yet have . <term> Semantic </term> and other	information	may still be incorporated , but as constraints	#15100 Semantic and other information may still be incorporated, but as constraints on the translation relation, not as levels of textual representation.
</term> puts different amounts and types of	information	into its <term> lexicon </term> according to	#15933 Although every natural language system needs a computational lexicon, each system puts different amounts and types of information into its lexicon according to its individual needs.
individual needs . However , some of the	information	needed across <term> systems </term> is shared	#15948 However, some of the information needed across systems is shared or identical information.
<term> systems </term> is shared or identical	information	. This paper presents our experience in	#15956 However, some of the information needed across systems is shared or identical information.
drawn primarily on explicit and implicit	information	from <term> machine-readable dictionaries	#16000 We have drawn primarily on explicit and implicit information from machine-readable dictionaries (MRD's) to create a broad coverage lexicon.
is and how it can be used . The types of	information	that a <term> user model </term> may be required	#16070 The types of information that a user model may be required to keep about a user are then identified and discussed.


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