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lr,12-2-P01-1008,bq	We present an <term> unsupervised learning algorithm </term> for <term> identification of paraphrases </term> from a <term> corpus of multiple English translations </term> of the same <term> source text </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	An evaluation of our <term> system </term> against the <term> annotated data </term> shows that , it successfully classifies 73.2 % in a <term> German corpus </term> of 2.284 <term> SRHs </term> as either coherent or incoherent ( given a <term> baseline </term> of 54.55 % ) .	#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	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> .	#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	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> .	#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	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> .	#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	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> .	#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	Then , a <term> Hidden Markov Model </term> is trained on a <term> corpus </term> automatically tagged by the first <term> learner </term> .	#3368 Then, a Hidden Markov Model is trained on acorpus automatically tagged by the first learner.
lr,19-2-N03-4010,bq	The demonstration will focus on how <term> JAVELIN </term> processes <term> questions </term> and retrieves the most likely <term> answer candidates </term> from the given <term> text corpus </term> .	#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	Previous research has demonstrated the utility of <term> clustering </term> in inducing <term> semantic verb classes </term> from undisambiguated <term> corpus data </term> .	#3899 Previous research has demonstrated the utility of clustering in inducing semantic verb classes from undisambiguatedcorpus data.
lr,22-2-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> .	#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	Our method is seeded by a small <term> manually segmented Arabic corpus </term> and uses it to bootstrap an <term> unsupervised algorithm </term> to build the <term> Arabic word segmenter </term> from a large <term> unsegmented Arabic corpus </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	Our method is seeded by a small <term> manually segmented Arabic corpus </term> and uses it to bootstrap an <term> unsupervised algorithm </term> to build the <term> Arabic word segmenter </term> from a large <term> unsegmented Arabic corpus </term> .	#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	The <term> language model </term> is initially 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	To improve the <term> segmentation </term><term> accuracy </term> , we use an <term> unsupervised algorithm </term> for automatically acquiring new <term> stems </term> from a 155 million <term> word </term><term> unsegmented corpus </term> , and re-estimate the <term> model parameters </term> with the expanded <term> vocabulary </term> and <term> training corpus </term> .	#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	To improve the <term> segmentation </term><term> accuracy </term> , we use an <term> unsupervised algorithm </term> for automatically acquiring new <term> stems </term> from a 155 million <term> word </term><term> unsegmented corpus </term> , and re-estimate the <term> model parameters </term> with the expanded <term> vocabulary </term> and <term> training corpus </term> .	#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	The resulting <term> Arabic word segmentation system </term> achieves around 97 % <term> exact match accuracy </term> on a <term> test corpus </term> containing 28,449 <term> word tokens </term> .	#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	We believe this is a state-of-the-art performance and the <term> algorithm </term> can be used for many <term> highly inflected languages </term> provided that one 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	We describe the ongoing construction of a large , <term> semantically annotated corpus </term> resource as reliable basis for the large-scale <term> acquisition of word-semantic information </term> , e.g. the construction of <term> domain-independent lexica </term> .	#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	We report experiments conducted on a <term> multilingual corpus </term> to estimate the number of <term> analogies </term> among the <term> sentences </term> that it contains .	#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	This paper proposes a new methodology to improve the <term> accuracy </term> of a <term> term aggregation system </term> using each author 's text as a coherent <term> corpus </term> .	#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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