Concordance

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

other,21-1-P03-1051,bq	</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 pattern prefix * - stem-suffix * (* denotes zero or more occurrences of a morpheme).
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 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 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.
model,4-3-P03-1051,bq	. The <term> algorithm </term> uses a <term>	trigram language model	</term> to determine the most probable <term>	#4674 The algorithm uses a trigram language model to determine the most probable morpheme sequence for a given input.
other,17-3-P03-1051,bq	morpheme sequence </term> for a given <term>	input	</term> . The <term> language model </term>	#4687 The algorithm uses a trigram language model to determine the most probable morpheme 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 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 resulting Arabic word segmentation system achieves around 97% exact match accuracy on a test corpus containing 28,449 word tokens.
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 a morpheme ).
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 large unsegmented Arabic corpus.
model,8-1-P03-1051,bq	Arabic 's rich morphology </term> by a <term>	model	</term> that a <term> word </term> consists	#4608 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,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 28,449 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 the Arabic word segmenter from a large unsegmented Arabic corpus.
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 probable morpheme sequence for a given input.
other,30-7-P03-1051,bq	manually segmented corpus </term> of the <term>	language	</term> of interest . A central problem	#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 the language of interest.
other,17-1-P03-1051,bq	word </term> consists of a sequence of <term>	morphemes	</term> in the <term> pattern </term><term>	#4617 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).


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