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model,5-3-C04-1103,bq	<term> joint source-channel transliteration	model	</term> , also called <term> n-gram transliteration	#5781 Under this framework, a joint source-channel transliteration model, also called n-gram transliteration model (ngram TM), is further proposed to model the transliteration process.
model,20-2-C04-1147,bq	<term> terms </term> , an <term> independence	model	</term> , and a <term> parametric affinity	#6342 The framework is composed of a novel algorithm to efficiently compute the co-occurrence distribution between pairs of terms, an independence model, and a parametric affinity model.
	response </term> . We have already proposed a	model	, <term> TDMT ( Transfer-Driven Machine Translation	#20233 We have already proposed a model, TDMT (Transfer-Driven Machine Translation), that translates a sentence utilizing examples effectively and performs accurate structural disambiguation and target word selection.
	by <term> extrapolation </term> . Thus , our	model	, <term> TDMT on APs </term> , meets the vital	#20351 Thus, our model, TDMT on APs, meets the vital requirements of spoken language translation.
model,3-4-C92-4207,bq	<term> world </term> . To reconstruct the <term>	model	</term> , the authors extract the <term> qualitative	#18457 To reconstruct themodel, the authors extract the qualitative spatial constraints from the text, and represent them as the numerical constraints on the spatial attributes of the entities.
model,14-2-C92-1055,bq	error </term> introduced by the <term> language	model	</term> , traditional <term> statistical approaches	#17836 Owing to the problem of insufficient training data and approximation error introduced by the language model, traditional statistical approaches, which resolve ambiguities by indirectly and implicitly using maximum likelihood method, fail to achieve high performance in real applications.
tech,4-5-A94-1007,bq	<term> English coordinate structure analysis	model	</term> , which provides <term> top-down scope	#19792 This paper presents an English coordinate structure analysis model, which provides top-down scope information of the correct syntactic structure by taking advantage of the symmetric patterns of the parallelism.
tech,20-4-C04-1112,bq	<term> accuracy </term> over the <term> wordform	model	</term> . Also , the <term> WSD system based	#6076 Testing the lemma-based model on the Dutch SENSEVAL-2 test data, we achieve a significant increase in accuracy over the wordform 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,25-2-C04-1147,bq	model </term> , and a <term> parametric affinity	model	</term> . In comparison with previous <term>	#6348 The framework is composed of a novel algorithm to efficiently compute the co-occurrence distribution between pairs of terms, an independence model, and a parametric affinity model.
model,14-1-C80-1073,bq	Network </term> as a procedural <term> dialog	model	</term> . The development of such a <term>	#12355 An attempt has been made to use an Augmented Transition Network as a procedural dialog model.
model,34-7-J05-1003,bq	were not included in the original <term>	model	</term> . The new <term> model </term> achieved	#8833 The method combined the log-likelihood under a baseline model (that of Collins [1999]) with evidence from an additional 500,000 features over parse trees that were not included in the originalmodel.
model,25-4-H94-1014,bq	</term> as compared to using a <term> trigram	model	</term> . This paper describes a method of	#21289 Using the BU recognition system, experiments show a 7% improvement in recognition accuracy with the mixture trigram models as compared to using a trigram model.
other,20-1-P06-2110,bq	word vectors </term> in the <term> vector space	model	</term> . Through two experiments , three	#11492 This paper examines what kind of similarity between words can be represented by what kind of word vectors in the vector space model.
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,25-3-P05-1034,bq	</term> , and train a <term> tree-based ordering	model	</term> . We describe an efficient <term> decoder	#9271 We align a parallel corpus, project the source dependency parse onto the target sentence, extract dependency treelet translation pairs, and train a tree-based ordering model.
tech,9-6-P05-1067,bq	time decoding algorithm </term> for the <term>	model	</term> . We evaluate the <term> outputs </term>	#9507 We introduce a polynomial time decoding algorithm for themodel.
model,44-4-N04-4028,bq	features </term> of the input in a <term> Markov	model	</term> . We implement several techniques	#6856 The information extraction system we evaluate is based on a linear-chain conditional random field (CRF), a probabilistic model which has performed well on information extraction tasks because of its ability to capture arbitrary, overlapping features of the input in a Markov model.
model,12-3-C04-1103,bq	also called <term> n-gram transliteration	model	( ngram TM ) </term> , is further proposed	#5787 Under this framework, a joint source-channel transliteration model, also called n-gram transliteration model (ngram TM), is further proposed to model the transliteration process.
model,7-7-J05-1003,bq	log-likelihood </term> under a <term> baseline	model	</term> ( that of <term> Collins [ 1999 ] </term>	#8807 The method combined the log-likelihood under a baseline model (that of Collins [1999]) with evidence from an additional 500,000 features over parse trees that were not included in the original model.


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