Concordance

Query sentence 42 (1,958.1 per million)

tech,15-1-N01-1003,bq	but distinct tasks , one of which is <term>	sentence	scoping </term> , i.e. the choice of <term>	#1308 Sentence planning is a set of inter-related but distinct tasks, one of which issentence scoping, i.e. the choice of syntactic structure for elementary speech acts and the decision of how to combine them into one or more sentences.
tech,9-2-N01-1003,bq	paper , we present <term> SPoT </term> , a <term>	sentence	planner </term> , and a new methodology for	#1344 In this paper, we present SPoT, asentence planner, and a new methodology for automatically training SPoT on the basis of feedback provided by human judges.
other,18-4-N01-1003,bq	potentially large list of possible <term>	sentence	plans </term> for a given <term> text-plan	#1392 First, a very simple, randomized sentence-plan-generator (SPG) generates a potentially large list of possiblesentence plans for a given text-plan input.
other,12-5-N01-1003,bq	SPR ) </term> ranks the list of output <term>	sentence	plans </term> , and then selects the top-ranked	#1412 Second, the sentence-plan-ranker (SPR) ranks the list of outputsentence plans, and then selects the top-ranked plan.
other,10-7-N01-1003,bq	<term> SPR </term> learns to select a <term>	sentence	plan </term> whose <term> rating </term> on average	#1443 We show that the trained SPR learns to select asentence plan whose rating on average is only 5% worse than the top human-ranked sentence plan.
other,23-7-N01-1003,bq	5 % worse than the <term> top human-ranked	sentence	plan </term> . In this paper , we compare	#1458 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.
tech,7-2-P01-1056,bq	experimentally evaluate a <term> trainable	sentence	planner </term> for a <term> spoken dialogue	#2058 In this paper We experimentally evaluate a trainable sentence planner for a spoken dialogue system by eliciting subjective human judgments.
tech,18-3-P01-1056,bq	generation component </term> , two <term> rule-based	sentence	planners </term> , and two <term> baseline	#2090 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.
tech,24-3-P01-1056,bq	sentence planners </term> , and two <term> baseline	sentence	planners </term> . We show that the <term>	#2096 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.
tech,4-4-P01-1056,bq	</term> . We show that the <term> trainable	sentence	planner </term> performs better than the <term>	#2104 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.
other,21-1-N03-1026,bq	Grammars ( LFG ) </term> to the domain of <term>	sentence	condensation </term> . Our <term> system </term>	#2805 We present an application of ambiguity packing and stochastic disambiguation techniques for Lexical-Functional Grammars (LFG) to the domain ofsentence condensation.
tech,18-3-N03-1026,bq	<term> summarization </term> quality of <term>	sentence	condensation systems </term> . An <term> experimental	#2856 Furthermore, we propose the use of standard parser evaluation methods for automatically evaluating the summarization quality ofsentence condensation systems.
other,13-2-N03-2017,bq	non-overlapping intervals in the <term> French	sentence	</term> . We evaluate the utility of this	#3257 It requires disjoint English phrases to be mapped to non-overlapping intervals in the French sentence.
tech,9-3-P03-1005,bq	<term> question classification </term> and <term>	sentence	alignment tasks </term> to evaluate its performance	#3848 We applied the proposed method to question classification andsentence alignment tasks to evaluate its performance as a similarity measure and a kernel function.
tech,1-1-C04-1128,bq	our approach is successful . While <term>	sentence	extraction </term> as an approach to <term>	#6203 Whilesentence extraction as an approach to summarization has been shown to work in documents of certain genres, because of the conversational nature of email communication where utterances are made in relation to one made previously, sentence extraction may not capture the necessary segments of dialogue that would make a summary coherent.
tech,38-1-C04-1128,bq	relation to one made previously , <term>	sentence	extraction </term> may not capture the necessary	#6240 While sentence extraction as an approach to summarization has been shown to work in documents of certain genres, because of the conversational nature of email communication where utterances are made in relation to one made previously,sentence extraction may not capture the necessary segments of dialogue that would make a summary coherent.
other,28-3-I05-5003,bq	matches and non-matches </term> in the <term>	sentence	</term> . Our results show that <term> MT evaluation	#8395 We also introduce a novel classification method based on PER which leverages part of speech information of the words contributing to the word matches and non-matches in thesentence.
other,12-2-J05-1003,bq	candidate parses </term> for each input <term>	sentence	</term> , with associated <term> probabilities	#8675 The base parser produces a set of candidate parses for each inputsentence, with associated probabilities that define an initial ranking of these parses.
other,14-3-P05-1034,bq	dependency parse </term> onto the target <term>	sentence	</term> , extract <term> dependency treelet	#9258 We align a parallel corpus, project the source dependency parse onto the targetsentence, extract dependency treelet translation pairs, and train a tree-based ordering model.
tech,13-1-P05-3025,bq	the process </term> of <term> translating a	sentence	</term> . The <term> method </term> allows a <term>	#9846 This paper describes a method of interactively visualizing and directing the process of translating a sentence.


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