| tech,4-1-P99-1080,ak | This paper discusses a <term> decision-tree approach </term> to the problem of assigning <term> probabilities </term> to <term> words </term> following a given <term> text </term> . | #31174 This paper discusses adecision-tree approach to the problem of assigning probabilities to words following a given text. | |
| measure(ment),11-1-P99-1080,ak | This paper discusses a <term> decision-tree approach </term> to the problem of assigning <term> probabilities </term> to <term> words </term> following a given <term> text </term> . | #31181 This paper discusses a decision-tree approach to the problem of assigningprobabilities to words following a given text. | |
| other,13-1-P99-1080,ak | This paper discusses a <term> decision-tree approach </term> to the problem of assigning <term> probabilities </term> to <term> words </term> following a given <term> text </term> . | #31183 This paper discusses a decision-tree approach to the problem of assigning probabilities towords following a given text. | |
| other,17-1-P99-1080,ak | This paper discusses a <term> decision-tree approach </term> to the problem of assigning <term> probabilities </term> to <term> words </term> following a given <term> text </term> . | #31187 This paper discusses a decision-tree approach to the problem of assigning probabilities to words following a giventext. | |
| model,4-2-P99-1080,ak | In contrast with previous <term> decision-tree language model attempts </term> , an <term> algorithm </term> for selecting <term> nearly optimal questions </term> is considered . | #31193 In contrast with previousdecision-tree language model attempts, an algorithm for selecting nearly optimal questions is considered. | |
| tech,10-2-P99-1080,ak | In contrast with previous <term> decision-tree language model attempts </term> , an <term> algorithm </term> for selecting <term> nearly optimal questions </term> is considered . | #31199 In contrast with previous decision-tree language model attempts, analgorithm for selecting nearly optimal questions is considered. | |
| other,13-2-P99-1080,ak | In contrast with previous <term> decision-tree language model attempts </term> , an <term> algorithm </term> for selecting <term> nearly optimal questions </term> is considered . | #31202 In contrast with previous decision-tree language model attempts, an algorithm for selectingnearly optimal questions is considered. | |
| model,1-3-P99-1080,ak | The <term> model </term> is to be tested on a standard task , <term> The Wall Street Journal </term> , allowing a fair comparison with the well-known <term> tri-gram model </term> . | #31209 Themodel is to be tested on a standard task, The Wall Street Journal, allowing a fair comparison with the well-known tri-gram model. | |
| lr-prod,11-3-P99-1080,ak | The <term> model </term> is to be tested on a standard task , <term> The Wall Street Journal </term> , allowing a fair comparison with the well-known <term> tri-gram model </term> . | #31219 The model is to be tested on a standard task,The Wall Street Journal, allowing a fair comparison with the well-known tri-gram model. | |
| model,23-3-P99-1080,ak | The <term> model </term> is to be tested on a standard task , <term> The Wall Street Journal </term> , allowing a fair comparison with the well-known <term> tri-gram model </term> . | #31231 The model is to be tested on a standard task, The Wall Street Journal, allowing a fair comparison with the well-knowntri-gram model. |
