Concordance

Query corpus 55 (2,564.2 per million)

lr,12-2-P01-1008,bq	identification of paraphrases </term> from a <term>	corpus	of multiple English translations </term>	#1789 We present an unsupervised learning algorithm for identification of paraphrases from acorpus of multiple English translations of the same source text.
lr,19-4-N03-1012,bq	successfully classifies 73.2 % in a <term> German	corpus	</term> of 2.284 <term> SRHs </term> as either	#2521 An evaluation of our system against the annotated data shows that, it successfully classifies 73.2% in a German corpus of 2.284 SRHs as either coherent or incoherent (given a baseline of 54.55%).
lr,13-1-N03-2006,bq	</term> based on a small-sized <term> bilingual	corpus	</term> , we use an out-of-domain <term> bilingual	#3093 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.
lr,20-1-N03-2006,bq	, we use an out-of-domain <term> bilingual	corpus	</term> and , in addition , the <term> language	#3100 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.
lr,33-1-N03-2006,bq	model </term> of an in-domain <term> monolingual	corpus	</term> . We conducted experiments with an	#3113 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.
lr,19-3-N03-2006,bq	of using an out-of-domain <term> bilingual	corpus	</term> and the possibility of using the <term>	#3143 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.
lr,10-5-N03-2025,bq	Markov Model </term> is trained on a <term>	corpus	</term> automatically tagged by the first	#3368 Then, a Hidden Markov Model is trained on acorpus automatically tagged by the first learner.
lr,19-2-N03-4010,bq	candidates </term> from the given <term> text	corpus	</term> . The operation of the <term> system	#3682 The demonstration will focus on how JAVELIN processes questions and retrieves the most likely answer candidates from the given text corpus.
other,15-1-P03-1009,bq	classes </term> from undisambiguated <term>	corpus	data </term> . We describe a new approach	#3899 Previous research has demonstrated the utility of clustering in inducing semantic verb classes from undisambiguatedcorpus data.
lr,22-2-P03-1050,bq	a small ( 10K sentences ) <term> parallel	corpus	</term> as its sole <term> training resources	#4469 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.
lr,7-2-P03-1051,bq	by a small <term> manually segmented Arabic	corpus	</term> and uses it to bootstrap an <term>	#4648 Our method is seeded by a small manually segmented Arabic corpus and uses it to bootstrap an unsupervised algorithm to build the Arabic word segmenter from a large unsegmented Arabic corpus.
lr,28-2-P03-1051,bq	</term> from a large <term> unsegmented Arabic	corpus	</term> . The <term> algorithm </term> uses a	#4668 Our method is seeded by a small manually segmented Arabic corpus and uses it to bootstrap an unsupervised algorithm to build the Arabic word segmenter from a large unsegmented Arabic corpus.
lr,9-4-P03-1051,bq	estimated from a small <term> manually segmented	corpus	</term> of about 110,000 <term> words </term>	#4700 The language model is initially estimated from a small manually segmented corpus of about 110,000 words.
lr,21-5-P03-1051,bq	million <term> word </term><term> unsegmented	corpus	</term> , and re-estimate the <term> model	#4728 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.
lr,34-5-P03-1051,bq	<term> vocabulary </term> and <term> training	corpus	</term> . The resulting <term> Arabic word	#4741 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.
lr,15-6-P03-1051,bq	exact match accuracy </term> on a <term> test	corpus	</term> containing 28,449 <term> word tokens	#4759 The resulting Arabic word segmentation system achieves around 97% exact match accuracy on a test corpus containing 28,449 word tokens.
lr,25-7-P03-1051,bq	can create a small <term> manually segmented	corpus	</term> of the <term> language </term> of interest	#4792 We believe this is a state-of-the-art performance and the algorithm can be used for many highly inflected languages provided that one can create a small manually segmented corpus of the language of interest.
lr,9-1-P03-1068,bq	of a large , <term> semantically annotated	corpus	</term> resource as reliable basis for the	#4943 We describe the ongoing construction of a large, semantically annotated corpus resource as reliable basis for the large-scale acquisition of word-semantic information, e.g. the construction of domain-independent lexica.
lr,6-3-C04-1106,bq	experiments conducted on a <term> multilingual	corpus	</term> to estimate the number of <term> analogies	#5916 We report experiments conducted on a multilingual corpus to estimate the number of analogies among the sentences that it contains.
lr,23-2-C04-1116,bq	each author 's text as a coherent <term>	corpus	</term> . Our approach is based on the idea	#6137 This paper proposes a new methodology to improve the accuracy of a term aggregation system using each author's text as a coherentcorpus.


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