Concordance

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

other,20-1-P03-1051,bq	sequence of <term> morphemes </term> in the <term>	pattern	</term> <term> prefix * - stem-suffix * </term>	#4620 We approximate Arabic's rich morphology by a model that a word consists of a sequence of morphemes in the pattern prefix-stem-suffix (* denotes zero or more occurrences of a morpheme).
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.
tech,1-3-P03-1051,bq	unsegmented Arabic corpus </term> . The <term>	algorithm	</term> uses a <term> trigram language model	#4671 The algorithm uses a trigram language model to determine the most probable morpheme sequence for a given input.
model,1-4-P03-1051,bq	for a given <term> input </term> . The <term>	language model	</term> is initially estimated from a small	#4690 The language model is initially estimated from a small manually segmented corpus of about 110,000 words.
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.
lr,9-4-P03-1051,bq	initially estimated from a small <term>	manually segmented corpus	</term> of about 110,000 <term> words </term>	#4698 The language model is initially estimated from a small manually segmented corpus of about 110,000 words.
other,15-4-P03-1051,bq	segmented corpus </term> of about 110,000 <term>	words	</term> . To improve the <term> segmentation	#4704 The language model is initially estimated from a small manually segmented corpus of about 110,000 words .
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.
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,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.
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,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.
measure(ment),4-5-P03-1051,bq	improve the <term> segmentation </term> <term>	accuracy	</term> , we use an <term> unsupervised algorithm	#4710 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,11-1-P03-1051,bq	</term> by a <term> model </term> that a <term>	word	</term> consists of a sequence of <term> morphemes	#4611 We approximate Arabic's rich morphology by a model that a word consists of a sequence of morphemes in the pattern prefix-stem-suffix (* denotes zero or more occurrences of a morpheme).
other,2-1-P03-1051,bq	stemmer </term> above . We approximate <term>	Arabic 's rich morphology	</term> by a <term> model </term> that a <term>	#4602 We approximate Arabic 's rich morphology by a model that a word consists of a sequence of morphemes in the pattern prefix-stem-suffix (* denotes zero or more occurrences of a morpheme).
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,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.
tech,3-5-P03-1051,bq	<term> words </term> . To improve the <term>	segmentation	</term> <term> accuracy </term> , we use an	#4709 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,15-7-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.
tech,17-2-P03-1051,bq	</term> and uses it to bootstrap an <term>	unsupervised algorithm	</term> to build the <term> Arabic word segmenter	#4655 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.


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