Concordance

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

tech,9-5-P03-1051,bq	</term><term> accuracy </term> , we use an <term>	unsupervised algorithm	</term> for automatically acquiring new <term>	#4715 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.
other,15-5-P03-1051,bq	</term> for automatically acquiring new <term>	stems	</term> from a 155 million <term> word </term>	#4721 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.
other,20-5-P03-1051,bq	<term> stems </term> from a 155 million <term>	word	</term> <term> unsegmented corpus </term> ,	#4726 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,21-5-P03-1051,bq	from a 155 million <term> word </term> <term>	unsegmented corpus	</term> , and re-estimate the <term> model	#4727 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.
other,27-5-P03-1051,bq	corpus </term> , and re-estimate the <term>	model parameters	</term> with the expanded <term> vocabulary	#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 the model parameters with the expanded vocabulary and training corpus.
other,32-5-P03-1051,bq	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 expanded vocabulary and training corpus.
lr,34-5-P03-1051,bq	expanded <term> vocabulary </term> and <term>	training corpus	</term> . The resulting <term> Arabic word	#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 and training corpus.
tech,2-6-P03-1051,bq	training corpus </term> . The resulting <term>	Arabic word segmentation system	</term> achieves around 97 % <term> exact match	#4745 The resulting Arabic 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	system </term> achieves around 97 % <term>	exact match accuracy	</term> on a <term> test corpus </term> containing	#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	<term> exact match accuracy </term> on a <term>	test corpus	</term> containing 28,449 <term> word tokens	#4758 The resulting Arabic word segmentation system achieves around 97% exact match accuracy on a test corpus containing 28,449 word tokens.
other,19-6-P03-1051,bq	test corpus </term> containing 28,449 <term>	word tokens	</term> . We believe this is a state-of-the-art	#4762 The resulting Arabic word segmentation system achieves around 97% exact match accuracy on a test corpus containing 28,449 word tokens.
tech,9-7-P03-1051,bq	state-of-the-art performance and the <term>	algorithm	</term> can be used for many <term> highly	#4774 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.
other,15-7-P03-1051,bq	algorithm </term> can be used for many <term>	highly inflected languages	</term> provided that one can create a small	#4780 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,25-7-P03-1051,bq	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 small manually segmented corpus of the language of interest.
other,30-7-P03-1051,bq	manually segmented corpus </term> of the <term>	language	</term> of interest . A central problem	#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 the language of interest.


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