Concordance

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

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 approximateArabic '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,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 thepattern 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,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).
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 themodel parameters with the expanded vocabulary and training corpus.
other,30-7-P03-1051,bq	manually segmented corpus </term> of the <term>	language	</term> of interest . A central problem of	#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.
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,35-1-P03-1051,bq	denotes zero or more occurrences of a <term>	morpheme	</term> ) . Our method is seeded by a small	#4635 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 amorpheme).
tech,1-3-P03-1051,bq	unsegmented Arabic corpus </term> . The <term>	algorithm	</term> uses a <term> trigram language model	#4671 Thealgorithm uses a trigram language model to determine the most probable morpheme sequence for a given input.
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,2-6-P03-1051,bq	training corpus </term> . The resulting <term>	Arabic word segmentation system	</term> achieves around 97 % <term> exact match	#4745 The resultingArabic word segmentation system achieves around 97% exact match accuracy on a test corpus containing 28,449 word tokens.
tech,22-2-P03-1051,bq	unsupervised algorithm </term> to build the <term>	Arabic word segmenter	</term> from a large <term> unsegmented Arabic	#4660 Our method is seeded by a small manually segmented Arabic corpus and uses it to bootstrap an unsupervised algorithm to build theArabic word segmenter from a large unsegmented Arabic corpus.
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.
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.
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 thealgorithm 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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