Concordance

Query how 34 >
Positive filter how 34 (1,585.2 per million)

	The question is , however , how an interesting information piece would be found in a <term> large database </term> .	#42 The question is, however, how an interesting information piece would be found in a large database.
	In this paper we show how two standard outputs from <term> information extraction ( IE ) systems </term> - <term> named entity annotations </term> and <term> scenario templates </term> - can be used to enhance access to <term> text collections </term> via a standard <term> text browser </term> .	#279 In this paper we show how two standard outputs from information extraction (IE) systems - named entity annotations and scenario templates - can be used to enhance access to text collections via a standard text browser.
	We describe how this information is used in a <term> prototype system </term> designed to support <term> information workers </term> ' access to a <term> pharmaceutical news archive </term> as part of their <term> industry watch </term> function .	#315 We describe how this information is used in a prototype system designed to support information workers' access to a pharmaceutical news archive as part of their industry watch function.
	We show how research in <term> generation </term> can be adapted to <term> dialog systems </term> , and how the high cost of hand-crafting <term> knowledge-based generation systems </term> can be overcome by employing <term> machine learning techniques </term> .	#997 We show how research in generation can be adapted to dialog systems, and how the high cost of hand-crafting knowledge-based generation systems can be overcome by employing machine learning techniques.
	We show how research in <term> generation </term> can be adapted to <term> dialog systems </term> , and how the high cost of hand-crafting <term> knowledge-based generation systems </term> can be overcome by employing <term> machine learning techniques </term> .	#1009 We show how research in generation can be adapted to dialog systems, and how the high cost of hand-crafting knowledge-based generation systems can be overcome by employing machine learning techniques.
	<term> Sentence planning </term> is a set of inter-related but distinct tasks , one of which is <term> sentence scoping </term> , i.e. the choice of <term> syntactic structure </term> for elementary <term> speech acts </term> and the decision of how to combine them into one or more <term> sentences </term> .	#1325 Sentence planning is a set of inter-related but distinct tasks, one of which is sentence scoping, i.e. the choice of syntactic structure for elementary speech acts and the decision of how to combine them into one or more sentences.
	In this paper , we show how <term> training data </term> can be supplemented with <term> text </term> from the <term> web </term> filtered to match the <term> style </term> and/or <term> topic </term> of the target <term> recognition task </term> , but also that it is possible to get bigger performance gains from the <term> data </term> by using <term> class-dependent interpolation </term> of <term> N-grams </term> .	#3034 In this paper, we show how training data can be supplemented with text from the web filtered to match the style and/or topic of the target recognition task, but also that it is possible to get bigger performance gains from the data by using class-dependent interpolation of N-grams.
	The demonstration will focus on how <term> JAVELIN </term> processes <term> questions </term> and retrieves the most likely <term> answer candidates </term> from the given <term> text corpus </term> .	#3667 The demonstration will focus on how JAVELIN processes questions and retrieves the most likely answer candidates from the given text corpus.
	Finally , we show how this new <term> tagger </term> achieves state-of-the-art results in a <term> supervised , non-training intensive framework </term> .	#5599 Finally, we show how this new tagger achieves state-of-the-art results in a supervised, non-training intensive framework.
	We demonstrate how errors in the <term> machine translations </term> of the input <term> Arabic documents </term> can be corrected by identifying and generating from such <term> redundancy </term> , focusing on <term> noun phrases </term> .	#7222 We demonstrate how errors in the machine translations of the input Arabic documents can be corrected by identifying and generating from such redundancy, focusing on noun phrases.
	Experimental results are presented , that demonstrate how the proposed <term> method </term> allows to better generalize from the <term> training data </term> .	#7421 Experimental results are presented, that demonstrate how the proposed method allows to better generalize from the training data.
	We incorporate this analysis into a <term> diagnostic tool </term> intended for <term> developers </term> of <term> machine translation systems </term> , and demonstrate how our application can be used by <term> developers </term> to explore <term> patterns </term> in <term> machine translation output </term> .	#7662 We incorporate this analysis into a diagnostic tool intended for developers of machine translation systems, and demonstrate how our application can be used by developers to explore patterns in machine translation output.
	The strength of our <term> approach </term> is that it allows a <term> tree </term> to be represented as an arbitrary set of <term> features </term> , without concerns about how these <term> features </term> interact or overlap and without the need to define a <term> derivation </term> or a <term> generative model </term> which takes these <term> features </term> into account .	#8734 The strength of our approach is that it allows a tree to be represented as an arbitrary set of features, without concerns about how these features interact or overlap and without the need to define a derivation or a generative model which takes these features into account.
	We show how <term> sampling </term> can be used to reduce the <term> retrieval time </term> by orders of magnitude with no loss in <term> translation quality </term> .	#9180 We show how sampling can be used to reduce the retrieval time by orders of magnitude with no loss in translation quality.
	Using <term> alignment techniques </term> from <term> phrase-based statistical machine translation </term> , we show how <term> paraphrases </term> in one <term> language </term> can be identified using a <term> phrase </term> in another language as a pivot .	#9698 Using alignment techniques from phrase-based statistical machine translation, we show how paraphrases in one language can be identified using a phrase in another language as a pivot.
	We define a <term> paraphrase probability </term> that allows <term> paraphrases </term> extracted from a <term> bilingual parallel corpus </term> to be ranked using <term> translation probabilities </term> , and show how it can be refined to take <term> contextual information </term> into account .	#9739 We define a paraphrase probability that allows paraphrases extracted from a bilingual parallel corpus to be ranked using translation probabilities, and show how it can be refined to take contextual information into account.
	First , we investigate how well the <term> addressee </term> of a <term> dialogue act </term> can be predicted based on <term> gaze </term> , <term> utterance </term> and <term> conversational context features </term> .	#10257 First, we investigate how well the addressee of a dialogue act can be predicted based on gaze, utterance and conversational context features.
	Furthermore , we will show how some <term> evaluation measures </term> can be improved by the introduction of <term> word-dependent substitution costs </term> .	#10390 Furthermore, we will show how some evaluation measures can be improved by the introduction of word-dependent substitution costs.
	<term> FERRET </term> utilizes a novel approach to <term> Q/A </term> known as <term> predictive questioning </term> which attempts to identify the <term> questions </term> ( and <term> answers </term> ) that <term> users </term> need by analyzing how a <term> user </term> interacts with a system while gathering information related to a particular scenario .	#11680 FERRET utilizes a novel approach to Q/A known as predictive questioning which attempts to identify the questions (and answers) that users need by analyzing how a user interacts with a system while gathering information related to a particular scenario.
	Unlike <term> logic </term> , the <term> theory </term> specifies how different information in <term> memory </term> affects the certainty of the conclusions drawn .	#11951 Unlike logic, the theory specifies how different information in memory affects the certainty of the conclusions drawn.


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