Concordance

Query P03-1051 35 (1,631.8 per million)

tech,9-5-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> .	#4715 To improve the segmentation accuracy, we use anunsupervised 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.
other,15-5-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> .	#4721 To improve the segmentation accuracy, we use an unsupervised algorithm for automatically acquiring newstems from a 155 million word unsegmented corpus, and re-estimate the model parameters with the expanded vocabulary and training corpus.
other,20-5-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> .	#4726 To improve the segmentation accuracy, we use an unsupervised algorithm for automatically acquiring new stems from a 155 millionword unsegmented corpus, and re-estimate the model parameters with the expanded vocabulary and training corpus.
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> .	#4727 To improve the segmentation accuracy, we use an unsupervised algorithm for automatically acquiring new stems from a 155 million wordunsegmented corpus, and re-estimate the model parameters with the expanded vocabulary and training corpus.
other,27-5-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> .	#4733 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 themodel parameters with the expanded vocabulary and training corpus.
other,32-5-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> .	#4738 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 expandedvocabulary 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> .	#4740 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 andtraining corpus.
tech,2-6-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> .	#4745 The resultingArabic word segmentation system achieves around 97% exact match accuracy on a test corpus containing 28,449 word tokens.
measure(ment),10-6-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> .	#4753 The resulting Arabic word segmentation system achieves around 97%exact match accuracy on a test corpus containing 28,449 word tokens.
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> .	#4758 The resulting Arabic word segmentation system achieves around 97% exact match accuracy on atest corpus containing 28,449 word tokens.
other,19-6-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> .	#4762 The resulting Arabic word segmentation system achieves around 97% exact match accuracy on a test corpus containing 28,449word tokens.
tech,9-7-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 .	#4774 We believe this is a state-of-the-art performance and thealgorithm can be used for many highly inflected languages provided that one can create a small manually segmented corpus of the language of interest.
other,15-7-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 .	#4780 We believe this is a state-of-the-art performance and the algorithm can be used for manyhighly inflected languages provided that one can create a small manually segmented corpus of the language of interest.
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 .	#4790 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 smallmanually segmented corpus of the language of interest.
other,30-7-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 .	#4795 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 thelanguage of interest.


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