Concordance

Query present 43 (2,004.8 per million)

	In this paper , we present <term> SPoT </term> , a <term> sentence planner </term> , and a new methodology for automatically training <term> SPoT </term> on the basis of <term> feedback </term> provided by <term> human judges </term> .	#1340 In this paper, we present SPoT, a sentence planner, and a new methodology for automatically training SPoT on the basis of feedback provided by human judges.
	We present an <term> unsupervised learning algorithm </term> for <term> identification of paraphrases </term> from a <term> corpus of multiple English translations </term> of the same <term> source text </term> .	#1778 We present an unsupervised learning algorithm for identification of paraphrases from a corpus of multiple English translations of the same source text.
	These <term> words </term> appear frequently enough in <term> dialog </term> to warrant serious <term> attention </term> , yet present <term> natural language search engines </term> perform poorly on <term> queries </term> containing them .	#1859 These words appear frequently enough in dialog to warrant serious attention, yet present natural language search engines perform poorly on queries containing them.
	We present our <term> multi-level answer resolution algorithm </term> that combines results from the <term> answering agents </term> at the <term> question , passage , and/or answer levels </term> .	#2373 We present our multi-level answer resolution algorithm that combines results from the answering agents at the question, passage, and/or answer levels.
	In this paper we present <term> ONTOSCORE </term> , a system for scoring sets of <term> concepts </term> on the basis of an <term> ontology </term> .	#2439 In this paper we present ONTOSCORE, a system for scoring sets of concepts on the basis of an ontology.
	We present an implementation of the <term> model </term> based on <term> finite-state models </term> , demonstrate the <term> model </term> 's ability to significantly reduce <term> character and word error rate </term> , and provide evaluation results involving <term> automatic extraction </term> of <term> translation lexicons </term> from <term> printed text </term> .	#2745 We present an implementation of the model based on finite-state models, demonstrate the model's ability to significantly reduce character and word error rate, and provide evaluation results involving automatic extraction of translation lexicons from printed text.
	We present an application of <term> ambiguity packing and stochastic disambiguation techniques </term> for <term> Lexical-Functional Grammars ( LFG ) </term> to the domain of <term> sentence condensation </term> .	#2785 We present an application of ambiguity packing and stochastic disambiguation techniques for Lexical-Functional Grammars (LFG) to the domain of sentence condensation.
	We present a new <term> part-of-speech tagger </term> that demonstrates the following ideas : ( i ) explicit use of both preceding and following <term> tag contexts </term> via a <term> dependency network representation </term> , ( ii ) broad use of <term> lexical features </term> , including <term> jointly conditioning on multiple consecutive words </term> , ( iii ) effective use of <term> priors </term> in <term> conditional loglinear models </term> , and ( iv ) fine-grained modeling of <term> unknown word features </term> .	#2910 We present a new part-of-speech tagger that demonstrates the following ideas: (i) explicit use of both preceding and following tag contexts via a dependency network representation, (ii) broad use of lexical features, including jointly conditioning on multiple consecutive words, (iii) effective use of priors in conditional loglinear models, and (iv) fine-grained modeling of unknown word features.
	We present a <term> syntax-based constraint </term> for <term> word alignment </term> , known as the <term> cohesion constraint </term> .	#3229 We present a syntax-based constraint for word alignment, known as the cohesion constraint.
	In this paper we present a novel , customizable <term> IE paradigm </term> that takes advantage of <term> predicate-argument structures </term> .	#3715 In this paper we present a novel, customizable IE paradigm that takes advantage of predicate-argument structures.
	We present a set of <term> features </term> designed for <term> pronoun resolution </term> in <term> spoken dialogue </term> and determine the most promising <term> features </term> .	#3999 We present a set of features designed for pronoun resolution in spoken dialogue and determine the most promising features.
	Based on these results , we present an <term> ECA </term> that uses <term> verbal and nonverbal grounding acts </term> to update <term> dialogue state </term> .	#5093 Based on these results, we present an ECA that uses verbal and nonverbal grounding acts to update dialogue state.
	We present a new <term> HMM tagger </term> that exploits <term> context </term> on both sides of a <term> word </term> to be tagged , and evaluate it in both the <term> unsupervised and supervised case </term> .	#5497 We present a new HMM tagger that exploits context on both sides of a word to be tagged, and evaluate it in both the unsupervised and supervised case.
	Along the way , we present the first comprehensive comparison of <term> unsupervised methods for part-of-speech tagging </term> , noting that published results to date have not been comparable across <term> corpora </term> or <term> lexicons </term> .	#5531 Along the way, we present the first comprehensive comparison of unsupervised methods for part-of-speech tagging, noting that published results to date have not been comparable across corpora or lexicons.
	Observing that the quality of the <term> lexicon </term> greatly impacts the <term> accuracy </term> that can be achieved by the <term> algorithms </term> , we present a method of <term> HMM training </term> that improves <term> accuracy </term> when training of <term> lexical probabilities </term> is unstable .	#5578 Observing that the quality of the lexicon greatly impacts the accuracy that can be achieved by the algorithms, we present a method of HMM training that improves accuracy when training of lexical probabilities is unstable.
	In this paper , we present a <term> corpus-based supervised word sense disambiguation ( WSD ) system </term> for <term> Dutch </term> which combines <term> statistical classification ( maximum entropy ) </term> with <term> linguistic information </term> .	#5984 In this paper, we present a corpus-based supervised word sense disambiguation (WSD) system for Dutch which combines statistical classification (maximum entropy) with linguistic information.
	We present a <term> text mining method </term> for finding <term> synonymous expressions </term> based on the <term> distributional hypothesis </term> in a set of coherent <term> corpora </term> .	#6093 We present a text mining method for finding synonymous expressions based on the distributional hypothesis in a set of coherent corpora.
	In this paper , we present our work on the detection of <term> question-answer pairs </term> in an <term> email conversation </term> for the task of <term> email summarization </term> .	#6262 In this paper, we present our work on the detection of question-answer pairs in an email conversation for the task of email summarization.
	We present a framework for the fast computation of <term> lexical affinity models </term> .	#6309 We present a framework for the fast computation of lexical affinity models.
	We present <term> Minimum Bayes-Risk ( MBR ) decoding </term> for <term> statistical machine translation </term> .	#6544 We present Minimum Bayes-Risk (MBR) decoding for statistical machine translation.


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