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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.
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 segmentationaccuracy, 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,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.
other,17-3-P03-1051,bq	morpheme sequence </term> for a given <term>	input	</term> . The <term> language model </term> is	#4687 The algorithm uses a trigram language model to determine the most probable morpheme sequence for a giveninput.
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.
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.
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,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,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,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,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.
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.
lr,7-2-P03-1051,bq	. Our method is seeded by a small <term>	manually segmented Arabic corpus	</term> and uses it to bootstrap an <term>	#4645 Our method is seeded by a smallmanually segmented Arabic corpus and uses it to bootstrap an unsupervised algorithm to build the Arabic word segmenter from a large unsegmented Arabic corpus.
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).
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.
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).
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.
other,12-3-P03-1051,bq	</term> to determine the most probable <term>	morpheme sequence	</term> for a given <term> input </term> . The	#4682 The algorithm uses a trigram language model to determine the most probablemorpheme sequence for a given input.
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,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.


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