| tech,0-1-N01-1003,ak | </term> and <term> key prediction </term> . <term> | Sentence | planning </term> is a set of inter-related | #1293 Our algorithm reported more than 99% accuracy in both language identification and key prediction.Sentence planning is a set of inter-related but distinct tasks, one of which is sentence 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,15-1-N01-1003,ak | 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. | |
| other,40-1-N01-1003,ak | how to combine them into one or more <term> | sentences | </term> . In this paper , we present <term> | #1333 Sentence planning is a set of inter-related but distinct tasks, one of which is sentence scoping, i.e. the choice of syntactic structure for elementary speech acts and the decision of how to combine them into one or moresentences. | |
| tech,9-2-N01-1003,ak | 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,ak | 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,11-5-N01-1003,ak | SPR ) </term> ranks the list of <term> output | sentence | plans </term> , and then selects the <term> | #1412 Second, the sentence-plan-ranker (SPR) ranks the list of output sentence plans, and then selects the top-ranked plan. | |
| other,10-7-N01-1003,ak | <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,ak | 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,ak | experimentally evaluate a <term> trainable | sentence | planner </term> for a <term> spoken dialogue | #2059 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,ak | generation component </term> , two <term> rule-based | sentence | planners </term> , and two <term> baseline | #2091 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,ak | sentence planners </term> , and two <term> baseline | sentence | planners </term> . We show that the <term> | #2097 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,ak | </term> . We show that the <term> trainable | sentence | planner </term> performs better than the <term> | #2105 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. | |
| tech,21-1-N03-1026,ak | Grammars ( LFG ) </term> to the domain of <term> | sentence | condensation </term> . Our system incorporates | #2806 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,ak | <term> summarization quality </term> of <term> | sentence | condensation systems </term> . An <term> experimental | #2857 Furthermore, we propose the use of standard parser evaluation methods for automatically evaluating the summarization quality ofsentence condensation systems. | |
| other,13-2-N03-2017,ak | non-overlapping intervals in the <term> French | sentence | </term> . We evaluate the utility of this | #3258 It requires disjoint English phrases to be mapped to non-overlapping intervals in the French sentence. | |
| tech,9-3-P03-1005,ak | <term> question classification </term> and <term> | sentence | alignment tasks </term> to evaluate its performance | #3849 We applied the proposed method to question classification andsentence alignment tasks to evaluate its performance as a similarity measure and a kernel function. | |
| lr,17-2-P03-1050,ak | English stemmer </term> and a <term> small ( 10K | sentences | ) parallel corpus </term> as its sole <term> | #4468 The stemming model is based on statistical machine translation and it uses an English stemmer and a small (10K sentences) parallel corpus as its sole training resources. | |
| tech,5-1-H05-1115,ak | consider the problem of <term> question-focused | sentence | retrieval </term> from complex news articles | #5755 We consider the problem of question-focused sentence retrieval from complex news articles describing multi-event stories published over time. | |
| other,2-4-H05-1115,ak | victims have been found ? ) Judges found <term> | sentences | </term> providing an <term> answer </term> to | #5809 Judges foundsentences providing an answer to each question. | |
| other,3-5-H05-1115,ak | <term> question </term> . To address the <term> | sentence | retrieval problem </term> , we apply a <term> | #5820 To address thesentence retrieval problem, we apply a stochastic, graph-based method for comparing the relative importance of the textual units, which was previously used successfully for generic summarization. |
