Concordance

Query task 31 (1,445.3 per million)

	human judges </term> . We reconceptualize the	task	into two distinct phases . First , a very	#1368 We reconceptualize the task into two distinct phases.
	</term> . We apply our <term> system </term> to the	task	of <term> scoring </term> alternative <term>	#2462 We apply our system to the task of scoring alternative speech recognition hypotheses (SRH) in terms of their semantic coherence.
tech,27-2-N03-2003,bq	topic </term> of the target <term> recognition	task	</term> , but also that it is possible to	#3056 In this paper, we show how training data can be supplemented with text from the web filtered to match the style and/or topic of the target recognition task, but also that it is possible to get bigger performance gains from the data by using class-dependent interpolation of N-grams.
	time finding more data relevant to their	task	, and gives them translingual reach into	#3619 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.
tech,13-2-P03-1030,bq	detection </term> as <term> information retrieval	task	</term> and hypothesize on the impact of <term>	#4078 In this paper we formulate story link detection and new event detection as information retrieval task and hypothesize on the impact of precision and recall on both systems.
other,29-2-P03-1058,bq	the <term> SENSEVAL-2 English lexical sample	task	</term> . Our investigation reveals that	#4854 In this paper, we evaluate an approach to automatically acquire sense-tagged training data from English-Chinese parallel corpora, which are then used for disambiguating the nouns in the SENSEVAL-2 English lexical sample task.
other,15-2-P03-1070,bq	in the context of a <term> direction-giving	task	</term> . The distribution of <term> nonverbal	#5056 We analyzed eye gaze, head nods and attentional focus in the context of a direction-giving task.
	an <term> email conversation </term> for the	task	of <term> email summarization </term> . We	#6277 In this paper, we present our work on the detection of question-answer pairs in an email conversation for the task of email summarization.
other,10-4-N04-1022,bq	on a <term> Chinese-to-English translation	task	</term> . Our results show that <term> MBR	#6624 We report the performance of the MBR decoders on a Chinese-to-English translation task.
	topic signatures </term> on a <term> WSD </term>	task	, where we trained a <term> second-order	#7002 We evaluated the topic signatures on a WSDtask, where we trained a second-order vector co-occurrence algorithm on standard WSD datasets, with promising results.
other,37-1-I05-2021,bq	the <term> Senseval-3 Chinese lexical sample	task	</term> . Much effort has been put in designing	#7827 We present the first known empirical test of an increasingly common speculative claim, by evaluating a representative Chinese-to-English SMT model directly on word sense disambiguation performance, using standard WSD evaluation methodology and datasets from the Senseval-3 Chinese lexical sample task.
	larger volume from the <term> Web </term> . The	task	of <term> machine translation ( MT ) evaluation	#8317 The task of machine translation (MT) evaluation is closely related to the task of sentence-level semantic equivalence classification.
	evaluation </term> is closely related to the	task	of <term> sentence-level semantic equivalence	#8330 The task of machine translation (MT) evaluation is closely related to the task of sentence-level semantic equivalence classification.
	Annotating <term> honorifics </term> is a complex	task	that involves identifying a <term> predicate	#8621 Annotating honorifics is a complex task that involves identifying a predicate with honorifics, assigning ranks to referents of the predicate, calibrating the ranks, and connecting referents with their predicates.
tech,7-5-J05-1003,bq	<term> method </term> for the <term> reranking	task	</term> , based on the <term> boosting approach	#8767 We introduce a new method for the reranking task, based on the boosting approach to ranking problems described in Freund et al. (1998).
other,9-5-P05-1067,bq	model </term> for the <term> machine translation	task	</term> , which can also be viewed as a <term>	#9485 Second, we describe the graphical model for the machine translation task, which can also be viewed as a stochastic tree-to-tree transducer.
tech,14-5-P05-1069,bq	standard <term> Arabic-English translation	task	</term> . Previous work has used <term> monolingual	#9652 The best system obtains a 18.6% improvement over the baseline on a standard Arabic-English translation task.
	<term> paraphrases </term> . We show that this	task	can be done using <term> bilingual parallel	#9671 We show that this task can be done using bilingual parallel corpora, a much more commonly available resource.
	indicators for the top-level prediction	task	. We also find that the <term> transcription	#10606 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.
	for such a <term> need </term> is a valuable	task	. We investigate that claim by adopting	#10760 Finding the preferred language for such a need is a valuable task.


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