Concordance

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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,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.
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,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.
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 smallmanually segmented corpus of the language of interest.
lr,9-4-P03-1051,bq	is 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 smallmanually segmented corpus of about 110,000 words.
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.
model,1-4-P03-1051,bq	for a given <term> input </term> . The <term>	language model	</term> is initially estimated from a small	#4690 Thelanguage model is initially estimated from a small manually segmented corpus of about 110,000 words.
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.
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.
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,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,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.
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.


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