Concordance

Query than 25 (1,165.6 per million)

	A <term> language learning experiment </term> showed that <term> assessors </term> can differentiate <term> native from non-native language essays </term> in less than 100 <term> words </term> .	#644 A language learning experiment showed that assessors can differentiate native from non-native language essays in less than 100 words.
	Our <term> algorithm </term> reported more than 99 % <term> accuracy </term> in both <term> language identification </term> and <term> key prediction </term> .	#1281 Our algorithm reported more than 99% accuracy in both language identification and key prediction.
	We show that the trained <term> SPR </term> learns to select a <term> sentence plan </term> whose <term> rating </term> on average is only 5 % worse than the <term> top human-ranked sentence plan </term> .	#1454 We show that the trained SPR learns to select a sentence plan whose rating on average is only 5% worse than the top human-ranked sentence plan.
other,18-1-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> .	#1835 This paper presents a formal analysis for a large class of words called alternative markers, which includes other ( than), such (as), and besides.
	We show that the <term> trainable sentence planner </term> performs better than the <term> rule-based systems </term> and the <term> baselines </term> , and as well as the <term> hand-crafted system </term> .	#2108 We show that the trainable sentence planner performs better than the rule-based systems and the baselines, and as well as the hand-crafted system.
	Surprisingly , learning <term> phrases </term> longer than three <term> words </term> and learning <term> phrases </term> from <term> high-accuracy word-level alignment models </term> does not have a strong impact on performance .	#2635 Surprisingly, learning phrases longer than three words and learning phrases from high-accuracy word-level alignment models does not have a strong impact on performance.
	For our purposes , a <term> hub </term> is a <term> node </term> in a <term> graph </term> with <term> in-degree </term> greater than one and <term> out-degree </term> greater than one .	#3184 For our purposes, a hub is a node in a graph with in-degree greater than one and out-degree greater than one.
	For our purposes , a <term> hub </term> is a <term> node </term> in a <term> graph </term> with <term> in-degree </term> greater than one and <term> out-degree </term> greater than one .	#3189 For our purposes, a hub is a node in a graph with in-degree greater than one and out-degree greater than one.
	In this paper , we describe a <term> phrase-based unigram model </term> for <term> statistical machine translation </term> that uses a much simpler set of <term> model parameters </term> than similar <term> phrase-based models </term> .	#3412 In this paper, we describe a phrase-based unigram model for statistical machine translation that uses a much simpler set of model parametersthan similar phrase-based models.
	We demonstrate that an approximation of <term> HPSG </term> produces a more effective <term> CFG filter </term> than that of <term> LTAG </term> .	#5135 We demonstrate that an approximation of HPSG produces a more effective CFG filterthan that of LTAG.
	Results indicate that the system yields higher performance than a <term> baseline </term> on all three aspects .	#6746 Results indicate that the system yields higher performance than a baseline on all three aspects.
	We present controlled experiments showing the <term> WSD </term><term> accuracy </term> of current typical <term> SMT models </term> to be significantly lower than that of all the dedicated <term> WSD models </term> considered .	#7934 We present controlled experiments showing the WSD accuracy of current typical SMT models to be significantly lower than that of all the dedicated WSD models considered.
	In this paper we describe a novel <term> data structure </term> for <term> phrase-based statistical machine translation </term> which allows for the <term> retrieval </term> of arbitrarily long <term> phrases </term> while simultaneously using less <term> memory </term> than is required by current <term> decoder </term> implementations .	#9149 In this paper we describe a novel data structure for phrase-based statistical machine translation which allows for the retrieval of arbitrarily long phrases while simultaneously using less memorythan is required by current decoder implementations.
	Using a state-of-the-art <term> Chinese word sense disambiguation model </term> to choose <term> translation candidates </term> for a typical <term> IBM statistical MT system </term> , we find that <term> word sense disambiguation </term> does not yield significantly better <term> translation quality </term> than the <term> statistical machine translation system </term> alone .	#9380 Using a state-of-the-art Chinese word sense disambiguation model to choose translation candidates for a typical IBM statistical MT system, we find that word sense disambiguation does not yield significantly better translation qualitythan the statistical machine translation system alone.
	We want to illustrate a framework less restrictive than earlier ones by allowing a <term> speaker </term> leeway in forming an <term> utterance </term> about a task and in determining the conversational vehicle to deliver it .	#14544 We want to illustrate a framework less restrictive than earlier ones by allowing a speaker leeway in forming an utterance about a task and in determining the conversational vehicle to deliver it.
	We show that the proposed approach is more describable than other approaches such as those employing a traditional <term> generative phonological approach </term> .	#16412 We show that the proposed approach is more describable than other approaches such as those employing a traditional generative phonological approach.
	In addition , combination of the <term> training speakers </term> is done by averaging the <term> statistics > </term> of <term> independently trained models </term> rather than the usual pooling of all the <term> speech data </term> from many <term> speakers </term> prior to <term> training </term> .	#17054 In addition, combination of the training speakers is done by averaging the statistics> of independently trained models rather than the usual pooling of all the speech data from many speakers prior to training.
	As each new <term> edge </term> is added to the <term> chart </term> , the algorithm checks only the topmost of the <term> edges </term> adjacent to it , rather than all such <term> edges </term> as in conventional treatments .	#17624 As each new edge is added to the chart, the algorithm checks only the topmost of the edges adjacent to it, rather than all such edges as in conventional treatments.
	A further <term> reduction in the search space </term> is achieved by using <term> semantic </term> rather than <term> syntactic categories </term> on the <term> terminal and non-terminal edges </term> , thereby reducing the amount of <term> ambiguity </term> and thus the number of <term> edges </term> , since only <term> edges </term> with a valid <term> semantic </term> interpretation are ever introduced .	#17716 A further reduction in the search space is achieved by using semantic rather than syntactic categories on the terminal and non-terminal edges, thereby reducing the amount of ambiguity and thus the number of edges, since only edges with a valid semantic interpretation are ever introduced.
	The <term> transfer phase </term> in <term> machine translation ( MT ) systems </term> has been considered to be more complicated than <term> analysis </term> and <term> generation </term> , since it is inherently a conglomeration of individual <term> lexical rules </term> .	#18062 The transfer phase in machine translation (MT) systems has been considered to be more complicated than analysis and generation, since it is inherently a conglomeration of individual lexical rules.


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