Concordance

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

other,21-1-P03-1051,bq	morphemes </term> in the <term> pattern </term><term>	prefix * - stem-suffix *	</term> ( * denotes zero or more occurrences	#4621 We approximate Arabic's rich morphology by a model that a word consists of a sequence of morphemes in the patternprefix * - stem-suffix * (* denotes zero or more occurrences of a morpheme).
tech,3-5-P03-1051,bq	<term> words </term> . To improve the <term>	segmentation	</term><term> accuracy </term> , we use an <term>	#4709 To improve thesegmentation 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 newstems 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	<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 atest 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 andtraining corpus.
model,4-3-P03-1051,bq	</term> . The <term> algorithm </term> uses a <term>	trigram language model	</term> to determine the most probable <term>	#4674 The algorithm uses atrigram language model to determine the most probable morpheme sequence for a given input.
lr,28-2-P03-1051,bq	word segmenter </term> from a large <term>	unsegmented Arabic corpus	</term> . The <term> algorithm </term> uses a	#4666 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 largeunsegmented Arabic 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 wordunsegmented corpus, and re-estimate the model parameters with the expanded vocabulary and training corpus.
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 anunsupervised algorithm to build the Arabic word segmenter from a large unsegmented Arabic corpus.
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 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,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 expandedvocabulary 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 aword consists of a sequence of morphemes in the pattern prefix-stem-suffix (* denotes zero or more occurrences of a morpheme).
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 millionword unsegmented corpus, and re-estimate the model parameters with the expanded vocabulary and training corpus.
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 28,449word tokens.
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 110,000words.


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