Concordance

Query such 41 (1,911.5 per million)

	Traditional <term> information retrieval techniques </term> use a <term> histogram </term> of <term> keywords </term> as the <term> document representation </term> but <term> oral communication </term> may offer additional <term> indices </term> such as the time and place of the rejoinder and the attendance .	#75 Traditional information retrieval techniques use a histogram of keywords as the document representation but oral communication may offer additional indicessuch as the time and place of the rejoinder and the attendance.
	An alternative <term> index </term> could be the activity such as discussing , planning , informing , story-telling , etc .	#95 An alternative index could be the activity such as discussing, planning, informing, story-telling, etc.
	<term> Emotions </term> and other <term> indices </term> such as the <term> dominance distribution of speakers </term> might be available on the <term> surface </term> and could be used directly .	#167 Emotions and other indicessuch as the dominance distribution of speakers might be available on the surface and could be used directly.
other,23-1-P01-1009,bq	This paper presents a <term> formal analysis </term> for a large class of <term> words </term> called <term> alternative markers </term> , which includes <term> other ( than ) </term> , <term> such ( as ) </term> , and <term> besides </term> .	#1838 This paper presents a formal analysis for a large class of words called alternative markers, which includes other (than),such (as), and besides.
	We then use the <term> predicates </term> of such <term> clauses </term> to create a set of <term> domain independent features </term> to annotate an input <term> dataset </term> , and run two different <term> machine learning algorithms </term> : <term> SLIPPER </term> , a <term> rule-based learning algorithm </term> , and <term> TiMBL </term> , a <term> memory-based system </term> .	#5188 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.
	However , such an approach does not work well when there is no distinctive <term> attribute </term> among <term> objects </term> .	#5633 However, such an approach does not work well when there is no distinctive attribute among objects.
	We conducted <term> psychological experiments </term> with 42 subjects to collect <term> referring expressions </term> in such situations , and built a <term> generation algorithm </term> based on the results .	#5698 We conducted psychological experiments with 42 subjects to collect referring expressions in such situations, and built a generation algorithm based on the results.
	<term> Information extraction techniques </term> automatically create <term> structured databases </term> from <term> unstructured data sources </term> , such as the <term> Web </term> or <term> newswire documents </term> .	#6766 Information extraction techniques automatically create structured databases from unstructured data sources, such as the Web or newswire documents.
	<term> Topic signatures </term> can be useful in a number of <term> Natural Language Processing ( NLP ) </term> applications , such as <term> Word Sense Disambiguation ( WSD ) </term> and <term> Text Summarisation </term> .	#6946 Topic signatures can be useful in a number of Natural Language Processing (NLP) applications, such as Word Sense Disambiguation (WSD) and Text Summarisation.
	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> .	#7241 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.
	A <term> method </term> for producing such <term> phrases </term> from a <term> word-aligned corpora </term> is proposed .	#7361 A method for producing such phrases from a word-aligned corpora is proposed.
	A <term> statistical translation model </term> is also presented that deals such <term> phrases </term> , as well as a <term> training method </term> based on the maximization of <term> translation accuracy </term> , as measured with the <term> NIST evaluation metric </term> .	#7379 A statistical translation model is also presented that deals such phrases, as well as a training method based on the maximization of translation accuracy, as measured with the NIST evaluation metric.
	Until now , the only way to assess the correctness of answers to such questions involves manual determination of whether an information nugget appears in a system 's response .	#7553 Until now, the only way to assess the correctness of answers to such questions involves manual determination of whether an information nugget appears in a system's response.
	Automatic <term> evaluation metrics </term> for <term> Machine Translation ( MT ) systems </term> , such as <term> BLEU </term> or <term> NIST </term> , are now well established .	#7689 Automatic evaluation metrics for Machine Translation (MT) systems, such as BLEU or NIST, are now well established.
	We first introduce our <term> approach </term> to inducing such a <term> grammar </term> from <term> parallel corpora </term> .	#9467 We first introduce our approach to inducing such a grammar from parallel corpora.
	In many cases though such movements still result in correct or almost correct <term> sentences </term> .	#10343 In many cases though such movements still result in correct or almost correct sentences.
	Examination of the effect of <term> features </term> shows that <term> predicting top-level and predicting subtopic boundaries </term> are two distinct tasks : ( 1 ) for predicting <term> subtopic boundaries </term> , the <term> lexical cohesion-based approach </term> alone can achieve competitive results , ( 2 ) for <term> predicting top-level boundaries </term> , the <term> machine learning approach </term> that combines <term> lexical-cohesion and conversational features </term> performs best , and ( 3 ) <term> conversational cues </term> , such as <term> cue phrases </term> and <term> overlapping speech </term> , are better indicators for the top-level prediction task .	#10591 Examination of the effect of features shows that predicting top-level and predicting subtopic boundaries are two distinct tasks: (1) for predicting subtopic boundaries, the lexical cohesion-based approach alone can achieve competitive results, (2) for predicting top-level boundaries, the machine learning approach that combines lexical-cohesion and conversational features performs best, and (3) conversational cues, such as cue phrases and overlapping speech, are better indicators for the top-level prediction task.
	This paper discusses two problems that arise in the <term> Generation of Referring Expressions </term> : ( a ) <term> numeric-valued attributes </term> , such as size or location ; ( b ) <term> perspective-taking in reference </term> .	#10666 This paper discusses two problems that arise in the Generation of Referring Expressions: (a) numeric-valued attributes, such as size or location; (b) perspective-taking in reference.
	Finding the preferred <term> language </term> for such a <term> need </term> is a valuable task .	#10754 Finding the preferred language for such a need is a valuable task.
	This formalism is both elementary and powerful enough to strongly simulate many <term> grammar formalisms </term> , such as <term> rewriting systems </term> , <term> dependency grammars </term> , <term> TAG </term> , <term> HPSG </term> and <term> LFG </term> .	#11104 This formalism is both elementary and powerful enough to strongly simulate many grammar formalisms, such as rewriting systems, dependency grammars, TAG, HPSG and LFG.


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