Concordance

Query present 43 (2,004.8 per million)

<term> sentences </term> . In this paper , we	present	<term> SPoT </term> , a <term> sentence planner	#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.
methods to collect <term> paraphrases </term> . We	present	an <term> unsupervised learning algorithm	#1778 We present an unsupervised learning algorithm for identification of paraphrases from a corpus of multiple English translations of the same source text.
warrant serious <term> attention </term> , yet	present	<term> natural language search engines </term>	#1859 These words appear frequently enough in dialog to warrant serious attention, yet present natural language search engines perform poorly on queries containing them.
adopting <term> statistical techniques </term> . We	present	our <term> multi-level answer resolution	#2373 We present our multi-level answer resolution algorithm that combines results from the answering agents at the question, passage, and/or answer levels.
precision metric </term> . In this paper we	present	<term> ONTOSCORE </term> , a system for scoring	#2439 In this paper we present ONTOSCORE, a system for scoring sets of concepts on the basis of an ontology.
more useful for <term> NLP tasks </term> . We	present	an implementation of the <term> model </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.
</term> from <term> printed text </term> . We	present	an application of <term> ambiguity packing	#2785 We present an application of ambiguity packing and stochastic disambiguation techniques for Lexical-Functional Grammars (LFG) to the domain of sentence condensation.
constraint-based parser/generator </term> . We	present	a new <term> part-of-speech tagger </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.
more complex mixtures of techniques . We	present	a <term> syntax-based constraint </term> for	#3229 We present a syntax-based constraint for word alignment, known as the cohesion constraint.
answering session </term> . In this paper we	present	a novel , customizable <term> IE paradigm	#3715 In this paper we present a novel, customizable IE paradigm that takes advantage of predicate-argument structures.
<term> NP - and non-NP-antecedents </term> . We	present	a set of <term> features </term> designed for	#3999 We present a set of features designed for pronoun resolution in spoken dialogue and determine the most promising features.
feedback </term> . Based on these results , we	present	an <term> ECA </term> that uses <term> verbal	#5093 Based on these results, we present an ECA that uses verbal and nonverbal grounding acts to update dialogue state.
significant positive effect on both tasks . We	present	a new <term> HMM tagger </term> that exploits	#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.
supervised case </term> . Along the way , we	present	the first comprehensive comparison of <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.
achieved by the <term> algorithms </term> , we	present	a method of <term> HMM training </term> that	#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.
<term> meanings </term> . In this paper , we	present	a <term> corpus-based supervised word sense	#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.
lemmas </term> is smaller and more robust . We	present	a <term> text mining method </term> for finding	#6093 We present a text mining method for finding synonymous expressions based on the distributional hypothesis in a set of coherent corpora.
summary </term> coherent . In this paper , we	present	our work on the detection of <term> question-answer	#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.
<term> question-answer pairing </term> . We	present	a framework for the fast computation of	#6309 We present a framework for the fast computation of lexical affinity models.
BalkaNet </term> and <term> EuroWordNet </term> . We	present	<term> Minimum Bayes-Risk ( MBR ) decoding	#6544 We present Minimum Bayes-Risk (MBR) decoding for statistical machine translation.


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