Concordance

Query two 39 (1,818.3 per million)

	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> .	#280 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.
	The <term> CCLINC Korean-to-English translation system </term> consists of two <term> core modules </term> , <term> language understanding and generation modules </term> mediated by a <term> language neutral meaning representation </term> called a <term> semantic frame </term> .	#418 The CCLINC Korean-to-English translation system consists of two core modules, language understanding and generation modules mediated by a language neutral meaning representation called a semantic frame.
	We reconceptualize the task into two distinct phases .	#1370 We reconceptualize the task into two distinct phases.
	Over two distinct <term> datasets </term> , we find that <term> indexing </term> according to simple <term> character bigrams </term> produces a <term> retrieval accuracy </term> superior to any of the tested <term> word N-gram models </term> .	#1532 Over two distinct datasets, we find that indexing according to simple character bigrams produces a retrieval accuracy superior to any of the tested word N-gram models.
	In order to perform an exhaustive comparison , we also evaluate a <term> hand-crafted template-based generation component </term> , two <term> rule-based sentence planners </term> , and two <term> baseline sentence planners </term> .	#2088 In order to perform an exhaustive comparison, we also evaluate a hand-crafted template-based generation component, two rule-based sentence planners, and two baseline sentence planners.
	In order to perform an exhaustive comparison , we also evaluate a <term> hand-crafted template-based generation component </term> , two <term> rule-based sentence planners </term> , and two <term> baseline sentence planners </term> .	#2094 In order to perform an exhaustive comparison, we also evaluate a hand-crafted template-based generation component, two rule-based sentence planners, and two baseline sentence planners.
	The two <term> evaluation measures </term> of the <term> BLEU score </term> and the <term> NIST score </term> demonstrated the effect of using an out-of-domain <term> bilingual corpus </term> and the possibility of using the <term> language model </term> .	#3124 The two evaluation measures of the BLEU score and the NIST score demonstrated the effect of using an out-of-domain bilingual corpus and the possibility of using the language model.
	We evaluate the utility of this <term> constraint </term> in two different <term> algorithms </term> .	#3267 We evaluate the utility of this constraint in two different algorithms.
	The <term> bootstrapping procedure </term> is implemented as training two <term> successive learners </term> .	#3341 The bootstrapping procedure is implemented as training two successive learners.
	<term> FSM </term> provides two strategies for <term> language understanding </term> and have a high accuracy but little robustness and flexibility .	#3518 FSM provides two strategies for language understanding and have a high accuracy but little robustness and flexibility.
	On a subset of the most difficult <term> SENSEVAL-2 nouns </term> , the <term> accuracy </term> difference between the two approaches is only 14.0 % , and the difference could narrow further to 6.5 % if we disregard the advantage that <term> manually sense-tagged data </term> have in their <term> sense coverage </term> .	#4884 On a subset of the most difficult SENSEVAL-2 nouns, the accuracy difference between the two approaches is only 14.0%, and the difference could narrow further to 6.5% if we disregard the advantage that manually sense-tagged data have in their sense coverage.
	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> .	#5206 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.
	We tested the <term> clustering and filtering processes </term> on <term> electronic newsgroup discussions </term> , and evaluated their performance by means of two experiments : coarse-level <term> clustering </term> and simple <term> information retrieval </term> .	#5470 We tested the clustering and filtering processes on electronic newsgroup discussions, and evaluated their performance by means of two experiments: coarse-level clustering and simple information retrieval.
	In this paper , a novel framework for <term> machine transliteration/back transliteration </term> that allows us to carry out <term> direct orthographical mapping ( DOM ) </term> between two different <term> languages </term> is presented .	#5767 In this paper, a novel framework for machine transliteration/back transliteration that allows us to carry out direct orthographical mapping (DOM) between two different languages is presented.
	We give two estimates , a lower one and a higher one .	#5932 We give two estimates, a lower one and a higher one.
	The correlation of the new <term> measure </term> with <term> human judgment </term> has been investigated systematically on two different <term> language pairs </term> .	#10419 The correlation of the new measure with human judgment has been investigated systematically on two different language pairs.
	We extend prior work in two ways .	#10485 We extend prior work in two ways.
	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 .	#10541 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.
	We also find that the <term> transcription errors </term> inevitable in <term> ASR output </term> have a negative impact on models that combine <term> lexical-cohesion and conversational features </term> , but do not change the general preference of approach for the two tasks .	#10643 We also find that the transcription errors inevitable in ASR output have a negative impact on models that combine lexical-cohesion and conversational features, but do not change the general preference of approach for the two tasks.
	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> .	#10649 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.


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