Concordance

component performance </term> . We describe our

use

of this approach in numerous fielded user

</term> . The <term> model </term> is designed for

use

in <term> error correction </term> , with a

the <term> system output </term> due to the

use

of a <term> constraint-based parser/generator

network representation </term> , ( ii ) broad

use

of <term> lexical features </term> , including

<term> small-sized bilingual corpus </term> , we

use

an <term> out-of-domain bilingual corpus </term>

</term> . Unlike conventional methods that

use

<term> hand-crafted rules </term> , the proposed

for that difference . In this paper , we

use

the <term> information redundancy </term> in

<term> reranking approaches </term> . We make

use

of a <term> conditional log-linear model </term>

establishes the equivalence between the standard

use

of <term> BLEU </term> in <term> word n-grams

high <term> accuracy </term> of the model , the

use

of <term> smoothing </term> in an <term> unlexicalized

<term> information retrieval techniques </term>	use	a <term> histogram </term> of <term> keywords	#59 Traditional information retrieval techniquesuse a histogram of keywords as the document representation but oral communication may offer additional indices such as the time and place of the rejoinder and the attendance.
component performance </term> . We describe our	use	of this approach in numerous fielded user	#1226 We describe our use of this approach in numerous fielded user studies conducted with the U.S. military.
natural language </term> , current systems	use	<term> manual or semi-automatic methods </term>	#1769 While paraphrasing is critical both for interpretation and generation of natural language, current systems use manual or semi-automatic methods to collect paraphrases.
</term> . The <term> model </term> is designed for	use	in <term> error correction </term> , with a	#2718 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.
selection </term> . Furthermore , we propose the	use	of standard <term> parser evaluation methods	#2844 Furthermore, we propose the use of standard parser evaluation methods for automatically evaluating the summarization quality of sentence condensation systems.
the <term> system output </term> due to the	use	of a <term> constraint-based parser/generator	#2904 Overall summarization quality of the proposed system is state-of-the-art, with guaranteed grammaticality of the system output due to the use of a constraint-based parser/generator.
demonstrates the following ideas : ( i ) explicit	use	of both preceding and following <term> tag	#2926 We present a new part-of-speech tagger that demonstrates the following ideas: (i) explicit use of both preceding and following tag contexts via a dependency network representation, (ii) broad use of lexical features, including jointly conditioning on multiple consecutive words, (iii) effective use of priors in conditional loglinear models, and (iv) fine-grained modeling of unknown word features.
network representation </term> , ( ii ) broad	use	of <term> lexical features </term> , including	#2944 We present a new part-of-speech tagger that demonstrates the following ideas: (i) explicit use of both preceding and following tag contexts via a dependency network representation, (ii) broad use of lexical features, including jointly conditioning on multiple consecutive words, (iii) effective use of priors in conditional loglinear models, and (iv) fine-grained modeling of unknown word features.
multiple consecutive words , ( iii ) effective	use	of <term> priors </term> in <term> conditional	#2961 We present a new part-of-speech tagger that demonstrates the following ideas: (i) explicit use of both preceding and following tag contexts via a dependency network representation, (ii) broad use of lexical features, including jointly conditioning on multiple consecutive words, (iii) effective use of priors in conditional loglinear models, and (iv) fine-grained modeling of unknown word features.
<term> small-sized bilingual corpus </term> , we	use	an <term> out-of-domain bilingual corpus </term>	#3097 In order to boost the translation quality of EBMT based on a small-sized bilingual corpus, we use an out-of-domain bilingual corpus and, in addition, the language model of an in-domain monolingual corpus.
</term> . During <term> decoding </term> , we	use	a <term> block unigram model </term> and a <term>	#3433 During decoding, we use a block unigram model and a word-based trigram language model.
</term> . Unlike conventional methods that	use	<term> hand-crafted rules </term> , the proposed	#4240 Unlike conventional methods that use hand-crafted rules, the proposed method enables easy design of the discourse understanding process.
the <term> segmentation accuracy </term> , we	use	an <term> unsupervised algorithm </term> for	#4715 To improve the segmentation accuracy, we use an unsupervised algorithm for automatically acquiring new stems from a 155 million word unsegmented corpus, and re-estimate the model parameters with the expanded vocabulary and training corpus.
for that difference . In this paper , we	use	the <term> information redundancy </term> in	#5156 In this paper, we use the information redundancy in multilingual input to correct errors in machine translation and thus improve the quality of multilingual summaries.
</term> in the input documents . Further , the	use	of multiple <term> machine translation systems	#5225 Further, the use of multiple machine translation systems provides yet more redundancy, yielding different ways to realize that information in English.
<term> reranking approaches </term> . We make	use	of a <term> conditional log-linear model </term>	#5447 We make use of a conditional log-linear model, with hidden variables representing the assignment of lexical items to word clusters or word senses.
</term> are described briefly , as well as the	use	of <term> ILIMP </term> in a modular <term> syntactic	#6211 Other tasks using the method developed for ILIMP are described briefly, as well as the use of ILIMP in a modular syntactic analysis system.
establishes the equivalence between the standard	use	of <term> BLEU </term> in <term> word n-grams	#6275 This study establishes the equivalence between the standard use of BLEU in word n-grams and its application at the character level.
at the <term> character level </term> . The	use	of <term> BLEU </term> at the <term> character	#6290 The use of BLEU at the character level eliminates the word segmentation problem: it makes it possible to directly compare commercial systems outputting unsegmented texts with, for instance, statistical MT systems which usually segment their outputs.
high <term> accuracy </term> of the model , the	use	of <term> smoothing </term> in an <term> unlexicalized	#8996 In addition to the high accuracy of the model, the use of smoothing in an unlexicalized parser allows us to better examine the interplay between smoothing and parsing results.


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