| tech,0-1-N01-1003,ak | </term> and <term> key prediction </term> . <term> | Sentence planning | </term> is a set of inter-related but distinct | #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> syntactic | #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,22-1-N01-1003,ak | scoping </term> , i.e. the choice of <term> | syntactic structure | </term> for <term> elementary speech acts </term> | #1315 Sentence planning is a set of inter-related but distinct tasks, one of which is sentence scoping, i.e. the choice ofsyntactic structure for elementary speech acts and the decision of how to combine them into one or more sentences. | |
| other,25-1-N01-1003,ak | <term> syntactic structure </term> for <term> | elementary speech acts | </term> and the decision of how to combine | #1318 Sentence planning is a set of inter-related but distinct tasks, one of which is sentence scoping, i.e. the choice of syntactic structure forelementary 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. | |
| tool,6-2-N01-1003,ak | </term> . In this paper , we present <term> | SPoT | </term> , a <term> sentence planner </term> , | #1341 In this paper, we presentSPoT, a sentence planner, and a new methodology for automatically training SPoT on the basis of feedback provided by human judges. | |
| tech,9-2-N01-1003,ak | paper , we present <term> SPoT </term> , a <term> | sentence planner | </term> , and a new methodology for automatically | #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. | |
| tool,19-2-N01-1003,ak | methodology for automatically training <term> | SPoT | </term> on the basis of <term> feedback </term> | #1354 In this paper, we present SPoT, a sentence planner, and a new methodology for automatically trainingSPoT on the basis of feedback provided by human judges. | |
| model,24-2-N01-1003,ak | training <term> SPoT </term> on the basis of <term> | feedback | </term> provided by <term> human judges </term> | #1359 In this paper, we present SPoT, a sentence planner, and a new methodology for automatically training SPoT on the basis offeedback provided by human judges. | |
| other,27-2-N01-1003,ak | of <term> feedback </term> provided by <term> | human judges | </term> . We reconceptualize the task into | #1362 In this paper, we present SPoT, a sentence planner, and a new methodology for automatically training SPoT on the basis of feedback provided byhuman judges. | |
| tech,6-4-N01-1003,ak | distinct phases . First , a very simple , <term> | randomized sentence-plan-generator ( SPG ) | </term> generates a potentially large list | #1380 First, a very simple,randomized sentence-plan-generator ( SPG ) generates a potentially large list of possible sentence plans for a given text-plan input. | |
| other,18-4-N01-1003,ak | potentially large list of possible <term> | sentence plans | </term> for a given <term> text-plan input </term> | #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,23-4-N01-1003,ak | <term> sentence plans </term> for a given <term> | text-plan input | </term> . Second , the <term> sentence-plan-ranker | #1397 First, a very simple, randomized sentence-plan-generator (SPG) generates a potentially large list of possible sentence plans for a giventext-plan input. | |
| tech,3-5-N01-1003,ak | text-plan input </term> . Second , the <term> | sentence-plan-ranker ( SPR ) | </term> ranks the list of <term> output sentence | #1403 Second, thesentence-plan-ranker ( SPR ) ranks the list of output sentence plans, and then selects the top-ranked plan. | |
| other,11-5-N01-1003,ak | sentence-plan-ranker ( SPR ) </term> ranks the list of <term> | output sentence plans | </term> , and then selects the <term> top-ranked | #1411 Second, the sentence-plan-ranker (SPR) ranks the list ofoutput sentence plans, and then selects the top-ranked plan. | |
| other,19-5-N01-1003,ak | plans </term> , and then selects the <term> | top-ranked plan | </term> . The <term> SPR </term> uses <term> ranking | #1419 Second, the sentence-plan-ranker (SPR) ranks the list of output sentence plans, and then selects thetop-ranked plan. | |
| tech,1-6-N01-1003,ak | the <term> top-ranked plan </term> . The <term> | SPR | </term> uses <term> ranking rules </term> automatically | #1423 TheSPR uses ranking rules automatically learned from training data. | |
| model,3-6-N01-1003,ak | plan </term> . The <term> SPR </term> uses <term> | ranking rules | </term> automatically learned from <term> training | #1425 The SPR usesranking rules automatically learned from training data. | |
| lr,8-6-N01-1003,ak | rules </term> automatically learned from <term> | training data | </term> . We show that the trained <term> SPR | #1430 The SPR uses ranking rules automatically learned fromtraining data. | |
| tech,5-7-N01-1003,ak | data </term> . We show that the trained <term> | SPR | </term> learns to select a <term> sentence | #1438 We show that the trainedSPR learns to select a sentence plan whose rating on average is only 5% worse than the top human-ranked sentence plan. |
