Concordance

Query algorithm 41 (1,911.5 per million)

tech,4-2-H01-1070,bq	paper also proposes <term> rule-reduction	algorithm	</term> applying <term> mutual information </term>	#1267 The paper also proposes rule-reduction algorithm applying mutual information to reduce the error-correction rules.
tech,1-3-H01-1070,bq	error-correction rules </term> . Our <term>	algorithm	</term> reported more than 99 % <term> accuracy	#1278 Ouralgorithm reported more than 99% accuracy in both language identification and key prediction.
tech,3-2-P01-1008,bq	We present an <term> unsupervised learning	algorithm	</term> for <term> identification of paraphrases	#1782 We present an unsupervised learning algorithm for identification of paraphrases from a corpus of multiple English translations of the same source text.
tech,31-2-P01-1047,bq	sensitive logic </term> , and a <term> learning	algorithm	</term> from <term> structured data </term> (	#1978 Our logical definition leads to a neat relation to categorial grammar, (yielding a treatment of Montague semantics), a parsing-as-deduction in a resource sensitive logic, and a learning algorithm from structured data (based on a typing-algorithm and type-unification).
tech,3-3-N03-1004,bq	present our <term> multi-level answer resolution	algorithm	</term> that combines results from the <term>	#2378 We present our multi-level answer resolution algorithm that combines results from the answering agents at the question, passage, and/or answer levels.
tech,6-4-N03-1004,bq	effectiveness of our <term> answer resolution	algorithm	</term> show a 35.0 % relative improvement	#2404 Experiments evaluating the effectiveness of our answer resolution algorithm show a 35.0% relative improvement over our baseline system in the number of questions correctly answered, and a 32.8% improvement according to the average precision metric.
tech,8-1-N03-1017,bq	translation model </term> and <term> decoding	algorithm	</term> that enables us to evaluate and compare	#2548 We propose a new phrase-based translation model and decoding algorithm that enables us to evaluate and compare several, previously proposed phrase-based translation models.
tech,17-2-P03-1051,bq	uses it to bootstrap an <term> unsupervised	algorithm	</term> to build the <term> Arabic word segmenter	#4656 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.
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,9-5-P03-1051,bq	accuracy </term> , we use an <term> unsupervised	algorithm	</term> for automatically acquiring new <term>	#4716 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,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,33-3-C04-1035,bq	SLIPPER </term> , a <term> rule-based learning	algorithm	</term> , and <term> TiMBL </term> , a <term> memory-based	#5217 We then use the predicates of such clauses to create a set of domain independent features to annotate an input dataset, and run two different machine learning algorithms: SLIPPER, a rule-based learning algorithm, and TiMBL, a memory-based system.
tech,18-5-C04-1096,bq	situations , and built a <term> generation	algorithm	</term> based on the results . The evaluation	#5705 We conducted psychological experiments with 42 subjects to collect referring expressions in such situations, and built a generation algorithm based on the results.
	training material </term> available to the	algorithm	. Testing the <term> lemma-based model </term>	#6053 The advantage of this novel method is that it clusters all inflected forms of an ambiguous word in one classifier, therefore augmenting the training material available to the algorithm.
	</term> . The framework is composed of a novel	algorithm	to efficiently compute the <term> co-occurrence	#6328 The framework is composed of a novel algorithm to efficiently compute the co-occurrence distribution between pairs of terms, an independence model, and a parametric affinity model.
tech,14-5-P04-2005,bq	a <term> second-order vector co-occurrence	algorithm	</term> on standard <term> WSD datasets </term>	#7011 We evaluated the topic signatures on a WSD task, where we trained a second-order vector co-occurrence algorithm on standard WSD datasets, with promising results.
tech,4-1-H05-1012,bq	presents a <term> maximum entropy word alignment	algorithm	</term> for <term> Arabic-English </term> based	#7257 This paper presents a maximum entropy word alignment algorithm for Arabic-English based on supervised training data.
tech,3-5-H05-1012,bq	</term> . <term> Performance </term> of the <term>	algorithm	</term> is contrasted with <term> human annotation	#7329 Performance of thealgorithm is contrasted with human annotation performance.
tech,6-9-J05-1003,bq	The article also introduces a new <term>	algorithm	</term> for the <term> boosting approach </term>	#8867 The article also introduces a newalgorithm for the boosting approach which takes advantage of the sparsity of the feature space in the parsing data.
tech,8-10-J05-1003,bq	significant efficiency gains for the new <term>	algorithm	</term> over the obvious <term> implementation	#8895 Experiments show significant efficiency gains for the newalgorithm over the obvious implementation of the boosting approach.


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