Concordance

Query two 39 (1,818.3 per million)

this purpose . In this paper we show how	two	standard outputs from <term> information	#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.
Korean-to-English translation system </term> consists of	two	<term> core modules </term> , <term> language	#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.
</term> . We reconceptualize the task into	two	distinct phases . First , a very simple	#1370 We reconceptualize the task into two distinct phases.
the form of <term> N-grams </term> ) . Over	two	distinct <term> datasets </term> , we find	#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.
template-based generation component </term> ,	two	<term> rule-based 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.
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.
experiments with an <term> EBMT system </term> . The	two	<term> evaluation measures </term> of the <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.
utility of this <term> constraint </term> in	two	different <term> algorithms </term> . The results	#3267 We evaluate the utility of this constraint in two different algorithms.
procedure </term> is implemented as training	two	<term> successive learners </term> . First	#3341 The bootstrapping procedure is implemented as training two successive learners.
( SLM ) </term> . <term> FSM </term> provides	two	strategies for <term> language understanding	#3518 FSM provides two strategies for language understanding and have a high accuracy but little robustness and flexibility.
<term> accuracy </term> difference between the	two	approaches is only 14.0 % , and the difference	#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.
annotate an input <term> dataset </term> , and run	two	different <term> machine learning algorithms	#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.
evaluated their performance by means of	two	experiments : coarse-level <term> clustering	#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.
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.
sentences </term> that it contains . We give	two	estimates , a lower one and a higher one	#5932 We give two estimates, a lower one and a higher one.
</term> has been investigated systematically on	two	different <term> language pairs </term> . The	#10419 The correlation of the new measure with human judgment has been investigated systematically on two different language pairs.
dialogue </term> . We extend prior work in	two	ways . We first apply approaches that have	#10485 We extend prior work in two ways.
predicting subtopic boundaries </term> are	two	distinct tasks : ( 1 ) for predicting <term>	#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.
the general preference of approach for the	two	tasks . This paper discusses two problems	#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.
for the two tasks . This paper discusses	two	problems that arise in the <term> Generation	#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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