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in less than 100 <term> words </term> . Even	more	illuminating was the factors on which the	#649 Even more illuminating was the factors on which the assessors made their decisions.
as <term> dialog systems </term> understand	more	of what the <term> user </term> tells them	#948 However, the improved speech recognition has brought to light a new problem: as dialog systems understand more of what the user tells them, they need to be more sophisticated at responding to the user.
<term> user </term> tells them , they need to be	more	sophisticated at responding to the <term>	#960 However, the improved speech recognition has brought to light a new problem: as dialog systems understand more of what the user tells them, they need to be more sophisticated at responding to the user.
</term> . Our <term> algorithm </term> reported	more	than 99 % <term> accuracy </term> in both <term>	#1280 Our algorithm reported more than 99% accuracy in both language identification and key prediction.
decision of how to combine them into one or	more	<term> sentences </term> . In this paper ,	#1332 Sentence planning is a set of inter-related but distinct tasks, one of which is sentence scoping, i.e. the choice of syntactic structure for elementary speech acts and the decision of how to combine them into one or more sentences.
<term> OCR systems </term> in order to make it	more	useful for <term> NLP tasks </term> . We present	#2738 The model is designed for use in error correction, with a focus on post-processing the output of black-box OCR systems in order to make it more useful for NLP tasks.
achieving similar <term> performance </term> to	more	complex mixtures of techniques . We present	#3222 Those hubs mark the boundary between root and suffix, achieving similar performance to more complex mixtures of techniques.
<term> Statistical approach </term> is much	more	robust but less accurate . <term> Cooperative	#3538 Statistical approach is much more robust but less accurate.
the ability to spend their time finding	more	data relevant to their task , and gives	#3614 It gives users the ability to spend their time finding more data relevant to their task, and gives them translingual reach into other languages by leveraging human language technology.
the <term> user model </term> we propose is	more	comprehensive . Specifically , we set up	#4319 Unlike previous studies that focus on user's knowledge or typical kinds of users, the user model we propose is more comprehensive.
stem-suffix * </term> ( * denotes zero or	more	occurrences of a <term> morpheme </term> )	#4631 We approximate Arabic's rich morphology by a model that a word consists of a sequence of morphemes in the pattern prefix-stem-suffix (* denotes zero or more occurrences of a morpheme).
approximation of <term> HPSG </term> produces a	more	effective <term> CFG filter </term> than that	#5131 We demonstrate that an approximation of HPSG produces a more effective CFG filter than that of LTAG.
system based on lemmas </term> is smaller and	more	robust . We present a <term> text mining	#6089 Also, the WSD system based on lemmas is smaller and more robust.
machine translation systems </term> provides yet	more	<term> redundancy </term> , yielding different	#7207 Further, the use of multiple machine translation systems provides yet more redundancy, yielding different ways to realize that information in English.
bilingual parallel corpora </term> , a much	more	commonly available <term> resource </term>	#9682 We show that this task can be done using bilingual parallel corpora, a much more commonly available resource.
computations involving the higher ( and	more	useful ) <term> models </term> are <term> hard	#10024 We prove that while IBM Models 1-2 are conceptually and computationally simple, computations involving the higher (and more useful) models are hard.
results can be improved using a bigger and a	more	homogeneous <term> corpus </term> to train	#11298 Finally, we have shown that these results can be improved using a bigger and a more homogeneous corpus to train, that is, a bigger corpus written by one unique author.
</term> . <term> Path-based inference </term> is	more	efficient , while <term> node-based inference	#12151 Path-based inference is more efficient, while node-based inference is more general.
while <term> node-based inference </term> is	more	general . A method is described of combining	#12158 Path-based inference is more efficient, while node-based inference is more general.
techniques and the still valuable methods of	more	traditional <term> natural language interfaces	#12660 The paper proposes interfaces based on a judicious mixture of these techniques and the still valuable methods of more traditional natural language interfaces.


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