Concordance

Query use 54 (2,517.6 per million)

<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 <term>	#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	manual or semi-automatic methods to collect	#1768 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	#2717 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	#2843 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	#2903 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	#2925 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	#2943 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.
consecutive words </term> , ( iii ) effective	use	of <term> priors </term> in <term> conditional	#2960 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.
small-sized <term> bilingual corpus </term> , we	use	an out-of-domain <term> bilingual corpus </term>	#3096 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>	#3432 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	#4238 Unlike conventional methods that use hand-crafted rules, the proposed method enables easy design of the discourse understanding process.
segmentation </term><term> accuracy </term> , we	use	an <term> unsupervised algorithm </term> for	#4713 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.
probabilistic Horn clauses </term> . We then	use	the <term> predicates </term> of such <term>	#5184 We then use the predicates of such clauses to create a set of domain independent features to annotate an input dataset, and run two different machine learning algorithms: SLIPPER, a rule-based learning algorithm, and TiMBL, a memory-based system.
eventual objective of this project is to	use	these <term> summaries </term> to assist <term>	#5415 The eventual objective of this project is to use these summaries to assist help-desk users and operators.
would not be enough numerous to be of any	use	. We report experiments conducted on a <term>	#5907 But computational linguists seem to be quite dubious about analogies between sentences: they would not be enough numerous to be of any use.
based on the idea that one person tends to	use	one <term> expression </term> for one <term>	#6151 Our approach is based on the idea that one person tends to use one expression for one meaning.
previous <term> models </term> , which either	use	arbitrary <term> windows </term> to compute	#6358 In comparison with previous models, which either use arbitrary windows to compute similarity between words or use lexical affinity to create sequential models, in this paper we focus on models intended to capture the co-occurrence patterns of any pair of words or phrases at any distance in the corpus.
similarity </term> between <term> words </term> or	use	<term> lexical affinity </term> to create <term>	#6367 In comparison with previous models, which either use arbitrary windows to compute similarity between words or use lexical affinity to create sequential models, in this paper we focus on models intended to capture the co-occurrence patterns of any pair of words or phrases at any distance in the corpus.
summarisation </term> . In this paper , we	use	the <term> information redundancy </term> in	#7130 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.


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