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tech,11-5-H01-1058,bq	show the need for a <term> dynamic language	model	combination </term> to improve the <term> performance	#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	first used to train a <term> phone n-gram	model	</term> for a particular <term> domain </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	of <term> recognition </term> with this <term>	model	</term> is then passed to a <term> phone-string	#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	propose a new <term> phrase-based translation	model	</term> and <term> decoding algorithm </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	probabilistic optical character recognition ( OCR )	model	</term> that describes an end-to-end process	#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	</term> of an <term> OCR system </term> . The <term>	model	</term> is designed for use in <term> error	#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	#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	finite-state models </term> , demonstrate the <term>	model	</term> 's ability to significantly reduce	#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	forests </term> , and a <term> maximum-entropy	model	</term> for <term> stochastic output selection	#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	</term> and , in addition , the <term> language	model	</term> of an in-domain <term> monolingual	#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 possibility of using the <term> language	model	</term> . We describe a simple <term> unsupervised	#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	we describe a <term> phrase-based unigram	model	</term> for <term> statistical machine translation	#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	</term> that uses a much simpler set of <term>	model	parameters </term> than similar <term> phrase-based	#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	decoding </term> , we use a <term> block unigram	model	</term> and a <term> word-based trigram language	#3436 During decoding, we use a block unigram model and a word-based trigram language model.
model,11-3-N03-2036,bq	</term> and a <term> word-based trigram language	model	</term> . During <term> training </term> , the	#3442 During decoding, we use a block unigram model and a word-based trigram language model.
model,16-2-P03-1033,bq	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	Arabic ) stemmer </term> . The <term> stemming	model	</term> is based on <term> statistical machine	#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	Arabic 's rich morphology </term> by a <term>	model	</term> that a <term> word </term> consists of	#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	algorithm </term> uses a <term> trigram language	model	</term> to determine the most probable <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	given <term> input </term> . The <term> language	model	</term> is initially estimated from a small	#4691 The language model is initially estimated from a small manually segmented corpus of about 110,000 words.


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