Concordance

Query model 90 (4,196.0 per million)

tech,11-5-H01-1058,bq	We provide experimental results that clearly show the need for a <term> dynamic language model combination </term> to improve the <term> performance </term> further .	#1144 We provide experimental results that clearly show the need for a dynamic language model combination to improve the performance further.
model,12-3-N03-1001,bq	In our method , <term> unsupervised training </term> is first used to train a <term> phone n-gram model </term> for a particular <term> domain </term> ; the <term> output </term> of <term> recognition </term> with this <term> model </term> is then passed to a <term> phone-string classifier </term> .	#2269 In our method, unsupervised training is first used to train a phone n-gram model for a particular domain; the output of recognition with this model is then passed to a phone-string classifier.
model,26-3-N03-1001,bq	In our method , <term> unsupervised training </term> is first used to train a <term> phone n-gram model </term> for a particular <term> domain </term> ; the <term> output </term> of <term> recognition </term> with this <term> model </term> is then passed to a <term> phone-string classifier </term> .	#2281 In our method, unsupervised training is first used to train a phone n-gram model for a particular domain; the output of recognition with thismodel is then passed to a phone-string classifier.
model,4-1-N03-1017,bq	We propose a new <term> phrase-based translation model </term> and <term> decoding algorithm </term> that enables us to evaluate and compare several , previously proposed <term> phrase-based translation models </term> .	#2545 We propose a new phrase-based translation model and decoding algorithm that enables us to evaluate and compare several, previously proposed phrase-based translation models.
model,7-1-N03-1018,bq	In this paper , we introduce a <term> generative probabilistic optical character recognition ( OCR ) model </term> that describes an end-to-end process in the <term> noisy channel framework </term> , progressing from generation of <term> true text </term> through its transformation into the <term> noisy output </term> of an <term> OCR system </term> .	#2682 In this paper, we introduce a generative probabilistic optical character recognition (OCR) model that describes an end-to-end process in the noisy channel framework, progressing from generation of true text through its transformation into the noisy output of an OCR system.
model,1-2-N03-1018,bq	The <term> model </term> is designed for use in <term> error correction </term> , with a focus on <term> post-processing </term> the <term> output </term> of black-box <term> OCR systems </term> in order to make it more useful for <term> NLP tasks </term> .	#2713 Themodel 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.
model,6-3-N03-1018,bq	We present an implementation of the <term> model </term> based on <term> finite-state models </term> , demonstrate the <term> model </term> 's ability to significantly reduce <term> character and word error rate </term> , and provide evaluation results involving <term> automatic extraction </term> of <term> translation lexicons </term> from <term> printed text </term> .	#2750 We present an implementation of themodel based on finite-state models, demonstrate the model's ability to significantly reduce character and word error rate, and provide evaluation results involving automatic extraction of translation lexicons from printed text.
model,14-3-N03-1018,bq	We present an implementation of the <term> model </term> based on <term> finite-state models </term> , demonstrate the <term> model </term> 's ability to significantly reduce <term> character and word error rate </term> , and provide evaluation results involving <term> automatic extraction </term> of <term> translation lexicons </term> from <term> printed text </term> .	#2758 We present an implementation of the model based on finite-state models, demonstrate themodel's ability to significantly reduce character and word error rate, and provide evaluation results involving automatic extraction of translation lexicons from printed text.
model,23-2-N03-1026,bq	Our <term> system </term> incorporates a <term> linguistic parser/generator </term> for <term> LFG </term> , a <term> transfer component </term> for <term> parse reduction </term> operating on <term> packed parse forests </term> , and a <term> maximum-entropy model </term> for <term> stochastic output selection </term> .	#2832 Our system incorporates a linguistic parser/generator for LFG, a transfer component for parse reduction operating on packed parse forests, and a maximum-entropy model for stochastic output selection.
model,28-1-N03-2006,bq	In order to boost the <term> translation quality </term> of <term> EBMT </term> based on a small-sized <term> bilingual corpus </term> , we use an out-of-domain <term> bilingual corpus </term> and , in addition , the <term> language model </term> of an in-domain <term> monolingual corpus </term> .	#3108 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.
model,27-3-N03-2006,bq	The two <term> evaluation measures </term> of the <term> BLEU score </term> and the <term> NIST score </term> demonstrated the effect of using an out-of-domain <term> bilingual corpus </term> and the possibility of using the <term> language model </term> .	#3151 The two evaluation measures of the BLEU score and the NIST score demonstrated the effect of using an out-of-domain bilingual corpus and the possibility of using the language model.
model,7-1-N03-2036,bq	In this paper , we describe a <term> phrase-based unigram model </term> for <term> statistical machine translation </term> that uses a much simpler set of <term> model parameters </term> than similar <term> phrase-based models </term> .	#3398 In this paper, we describe a phrase-based unigram model for statistical machine translation that uses a much simpler set of model parameters than similar phrase-based models.
other,21-1-N03-2036,bq	In this paper , we describe a <term> phrase-based unigram model </term> for <term> statistical machine translation </term> that uses a much simpler set of <term> model parameters </term> than similar <term> phrase-based models </term> .	#3410 In this paper, we describe a phrase-based unigram model for statistical machine translation that uses a much simpler set ofmodel parameters than similar phrase-based models.
model,6-3-N03-2036,bq	During <term> decoding </term> , we use a <term> block unigram model </term> and a <term> word-based trigram language model </term> .	#3436 During decoding, we use a block unigram model and a word-based trigram language model.
model,11-3-N03-2036,bq	During <term> decoding </term> , we use a <term> block unigram model </term> and a <term> word-based trigram language model </term> .	#3442 During decoding, we use a block unigram model and a word-based trigram language model.
model,16-2-P03-1033,bq	Unlike previous studies that focus on <term> user </term> 's <term> knowledge </term> or typical kinds of <term> users </term> , the <term> user model </term> we propose is more comprehensive .	#4315 Unlike previous studies that focus on user's knowledge or typical kinds of users, the user model we propose is more comprehensive.
model,1-2-P03-1050,bq	The <term> stemming model </term> is based on <term> statistical machine translation </term> and it uses an <term> English stemmer </term> and a small ( 10K sentences ) <term> parallel corpus </term> as its sole <term> training resources </term> .	#4448 The stemming model is based on statistical machine translation and it uses an English stemmer and a small (10K sentences) parallel corpus as its sole training resources.
model,8-1-P03-1051,bq	We approximate <term> Arabic 's rich morphology </term> by a <term> model </term> that a <term> word </term> consists of a sequence of <term> morphemes </term> in the <term> pattern </term><term> prefix * - stem-suffix * </term> ( * denotes zero or more occurrences of a <term> morpheme </term> ) .	#4608 We approximate Arabic's rich morphology by amodel that a word consists of a sequence of morphemes in the pattern prefix-stem-suffix (* denotes zero or more occurrences of a morpheme).
model,4-3-P03-1051,bq	The <term> algorithm </term> uses a <term> trigram language model </term> to determine the most probable <term> morpheme sequence </term> for a given <term> input </term> .	#4676 The algorithm uses a trigram language model to determine the most probable morpheme sequence for a given input.
model,1-4-P03-1051,bq	The <term> language model </term> is initially estimated from a small <term> manually segmented corpus </term> of about 110,000 <term> words </term> .	#4691 The language model is initially estimated from a small manually segmented corpus of about 110,000 words.


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