Concordance

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	<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> .	#1329 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.
	We reconceptualize the task into two distinct phases .	#1369 We reconceptualize the task into two distinct phases.
	In particular , <term> range concatenation languages [ RCL ] </term> can be parsed in <term> polynomial time </term> and many classical <term> grammatical formalisms </term> can be translated into equivalent <term> RCGs </term> without increasing their <term> worst-case parsing time complexity </term> .	#1644 In particular, range concatenation languages [RCL] can be parsed in polynomial time and many classical grammatical formalisms can be translated into equivalent RCGs without increasing their worst-case parsing time complexity.
	For example , after <term> translation </term> into an equivalent <term> RCG </term> , any <term> tree adjoining grammar </term> can be parsed in <term> O ( n6 ) time </term> .	#1660 For example, after translationinto an equivalent RCG, any tree adjoining grammar can be parsed in O(n6) time.
	In this paper , we introduce a <term> generative probabilistic optical character recognition ( OCR ) model </term> that describes an end-to-end process in the <term> noisy channel framework </term> , progressing from generation of <term> true text </term> through its transformation into the <term> noisy output </term> of an <term> OCR system </term> .	#2703 In this paper, we introduce a generative probabilistic optical character recognition (OCR) model that describes an end-to-end process in the noisy channel framework, progressing from generation of true text through its transformation into the noisy output of an OCR system.
	We create a <term> word-trie </term> , transform it into a <term> minimal DFA </term> , then identify <term> hubs </term> .	#3199 We create a word-trie, transform it into a minimal DFA, then identify hubs.
	It gives users the ability to spend their time finding more data relevant to their task , and gives them translingual reach into other <term> languages </term> by leveraging <term> human language technology </term> .	#3626 It gives users the ability to spend their time finding more data relevant to their task, and gives them translingual reach into other languages by leveraging human language technology.
	A novel <term> evaluation scheme </term> is proposed which accounts for the effect of <term> polysemy </term> on the <term> clusters </term> , offering us a good insight into the potential and limitations of <term> semantically classifying </term><term> undisambiguated SCF data </term> .	#3961 A novel evaluation scheme is proposed which accounts for the effect of polysemy on the clusters, offering us a good insight into the potential and limitations of semantically classifying undisambiguated SCF 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> .	#7648 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.
	Theory will be put into practice .	#8117 Theory will be put into practice.
	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 .	#8756 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 featuresinto account.
	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 .	#9748 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 informationinto account.
	Our work aims at providing useful insights into the the <term> computational complexity </term> of those problems .	#9996 Our work aims at providing useful insights into the the computational complexity of those problems.
	This paper describes <term> FERRET </term> , an <term> interactive question-answering ( Q/A ) system </term> designed to address the challenges of integrating <term> automatic Q/A </term> applications into real-world environments .	#11650 This paper describes FERRET, an interactive question-answering (Q/A) system designed to address the challenges of integrating automatic Q/A applications into real-world environments.
	The <term> LOGON MT demonstrator </term> assembles independently valuable <term> general-purpose NLP components </term> into a <term> machine translation pipeline </term> that capitalizes on <term> output quality </term> .	#11799 The LOGON MT demonstrator assembles independently valuable general-purpose NLP componentsinto a machine translation pipeline that capitalizes on output quality.
	With the aid of a <term> logic-based grammar formalism </term> called <term> extraposition grammars </term> , <term> Chat-80 </term> translates <term> English questions </term> into the <term> Prolog </term><term> subset of logic </term> .	#12902 With the aid of a logic-based grammar formalism called extraposition grammars, Chat-80 translates English questionsinto the Prolog subset of logic.
	The resulting <term> logical expression </term> is then transformed by a <term> planning algorithm </term> into efficient <term> Prolog </term> , cf. <term> query optimisation </term> in a <term> relational database </term> .	#12920 The resulting logical expression is then transformed by a planning algorithminto efficient Prolog, cf. query optimisation in a relational database.
	This method of using <term> expectations </term> to aid the understanding of <term> scruffy texts </term> has been incorporated into a working <term> computer program </term> called <term> NOMAD </term> , which understands <term> scruffy texts </term> in the domain of Navy messages .	#13114 This method of using expectations to aid the understanding of scruffy texts has been incorporated into a working computer program called NOMAD, which understands scruffy texts in the domain of Navy messages.
	The process of transforming a <term> disposition </term> into a <term> proposition </term> is referred to as <term> explicitation </term> or <term> restoration </term> .	#13591 The process of transforming a dispositioninto a proposition is referred to as explicitation or restoration.
	The <term> linguistic structure </term> consists of segments of the <term> discourse </term> into which the <term> utterances </term> naturally aggregate .	#14165 The linguistic structure consists of segments of the discourseinto which the utterances naturally aggregate.


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