Concordance

Query H01-1058 27 (864.4 per million)

model,11-1-H01-1058,ak	In this paper , we address the problem of combining several <term> language models ( LMs ) </term> .	#1038 In this paper, we address the problem of combining severallanguage models ( LMs ).
tech,4-2-H01-1058,ak	We find that simple <term> interpolation methods </term> , like <term> log-linear and linear interpolation </term> , improve the <term> performance </term> but fall short of the <term> performance </term> of an <term> oracle </term> .	#1048 We find that simpleinterpolation methods, like log-linear and linear interpolation, improve the performance but fall short of the performance of an oracle.
tech,8-2-H01-1058,ak	We find that simple <term> interpolation methods </term> , like <term> log-linear and linear interpolation </term> , improve the <term> performance </term> but fall short of the <term> performance </term> of an <term> oracle </term> .	#1052 We find that simple interpolation methods, likelog-linear and linear interpolation, improve the performance but fall short of the performance of an oracle.
measure(ment),15-2-H01-1058,ak	We find that simple <term> interpolation methods </term> , like <term> log-linear and linear interpolation </term> , improve the <term> performance </term> but fall short of the <term> performance </term> of an <term> oracle </term> .	#1059 We find that simple interpolation methods, like log-linear and linear interpolation, improve theperformance but fall short of the performance of an oracle.
measure(ment),21-2-H01-1058,ak	We find that simple <term> interpolation methods </term> , like <term> log-linear and linear interpolation </term> , improve the <term> performance </term> but fall short of the <term> performance </term> of an <term> oracle </term> .	#1065 We find that simple interpolation methods, like log-linear and linear interpolation, improve the performance but fall short of theperformance of an oracle.
tech,24-2-H01-1058,ak	We find that simple <term> interpolation methods </term> , like <term> log-linear and linear interpolation </term> , improve the <term> performance </term> but fall short of the <term> performance </term> of an <term> oracle </term> .	#1068 We find that simple interpolation methods, like log-linear and linear interpolation, improve the performance but fall short of the performance of anoracle.
tech,1-3-H01-1058,ak	The <term> oracle </term> knows the <term> reference word string </term> and selects the <term> word string </term> with the best <term> performance </term> ( typically , <term> word or semantic error rate </term> ) from a list of <term> word strings </term> , where each <term> word string </term> has been obtained by using a different <term> LM </term> .	#1071 Theoracle knows the reference word string and selects the word string with the best performance (typically, word or semantic error rate) from a list of word strings, where each word string has been obtained by using a different LM.
other,4-3-H01-1058,ak	The <term> oracle </term> knows the <term> reference word string </term> and selects the <term> word string </term> with the best <term> performance </term> ( typically , <term> word or semantic error rate </term> ) from a list of <term> word strings </term> , where each <term> word string </term> has been obtained by using a different <term> LM </term> .	#1074 The oracle knows thereference word string and selects the word string with the best performance (typically, word or semantic error rate) from a list of word strings, where each word string has been obtained by using a different LM.
other,10-3-H01-1058,ak	The <term> oracle </term> knows the <term> reference word string </term> and selects the <term> word string </term> with the best <term> performance </term> ( typically , <term> word or semantic error rate </term> ) from a list of <term> word strings </term> , where each <term> word string </term> has been obtained by using a different <term> LM </term> .	#1080 The oracle knows the reference word string and selects theword string with the best performance (typically, word or semantic error rate) from a list of word strings, where each word string has been obtained by using a different LM.
measure(ment),15-3-H01-1058,ak	The <term> oracle </term> knows the <term> reference word string </term> and selects the <term> word string </term> with the best <term> performance </term> ( typically , <term> word or semantic error rate </term> ) from a list of <term> word strings </term> , where each <term> word string </term> has been obtained by using a different <term> LM </term> .	#1085 The oracle knows the reference word string and selects the word string with the bestperformance (typically, word or semantic error rate) from a list of word strings, where each word string has been obtained by using a different LM.
measure(ment),19-3-H01-1058,ak	The <term> oracle </term> knows the <term> reference word string </term> and selects the <term> word string </term> with the best <term> performance </term> ( typically , <term> word or semantic error rate </term> ) from a list of <term> word strings </term> , where each <term> word string </term> has been obtained by using a different <term> LM </term> .	#1089 The oracle knows the reference word string and selects the word string with the best performance (typically,word or semantic error rate) from a list of word strings, where each word string has been obtained by using a different LM.
other,29-3-H01-1058,ak	The <term> oracle </term> knows the <term> reference word string </term> and selects the <term> word string </term> with the best <term> performance </term> ( typically , <term> word or semantic error rate </term> ) from a list of <term> word strings </term> , where each <term> word string </term> has been obtained by using a different <term> LM </term> .	#1099 The oracle knows the reference word string and selects the word string with the best performance (typically, word or semantic error rate) from a list ofword strings, where each word string has been obtained by using a different LM.
other,34-3-H01-1058,ak	The <term> oracle </term> knows the <term> reference word string </term> and selects the <term> word string </term> with the best <term> performance </term> ( typically , <term> word or semantic error rate </term> ) from a list of <term> word strings </term> , where each <term> word string </term> has been obtained by using a different <term> LM </term> .	#1104 The oracle knows the reference word string and selects the word string with the best performance (typically, word or semantic error rate) from a list of word strings, where eachword string has been obtained by using a different LM.
model,43-3-H01-1058,ak	The <term> oracle </term> knows the <term> reference word string </term> and selects the <term> word string </term> with the best <term> performance </term> ( typically , <term> word or semantic error rate </term> ) from a list of <term> word strings </term> , where each <term> word string </term> has been obtained by using a different <term> LM </term> .	#1113 The oracle knows the reference word string and selects the word string with the best performance (typically, word or semantic error rate) from a list of word strings, where each word string has been obtained by using a differentLM.
tech,3-4-H01-1058,ak	Actually , the <term> oracle </term> acts like a <term> dynamic combiner </term> with hard decisions using the <term> reference </term> .	#1118 Actually, theoracle acts like a dynamic combiner with hard decisions using the reference.
tech,7-4-H01-1058,ak	Actually , the <term> oracle </term> acts like a <term> dynamic combiner </term> with hard decisions using the <term> reference </term> .	#1122 Actually, the oracle acts like adynamic combiner with hard decisions using the reference.
model,14-4-H01-1058,ak	Actually , the <term> oracle </term> acts like a <term> dynamic combiner </term> with hard decisions using the <term> reference </term> .	#1129 Actually, the oracle acts like a dynamic combiner with hard decisions using thereference.
tech,11-5-H01-1058,ak	We provide experimental results that clearly show the need for a <term> dynamic language model combination </term> to improve the <term> performance </term> further .	#1142 We provide experimental results that clearly show the need for adynamic language model combination to improve the performance further.
measure(ment),18-5-H01-1058,ak	We provide experimental results that clearly show the need for a <term> dynamic language model combination </term> to improve the <term> performance </term> further .	#1149 We provide experimental results that clearly show the need for a dynamic language model combination to improve theperformance further.
tech,10-6-H01-1058,ak	We suggest a method that mimics the behavior of the <term> oracle </term> using a <term> neural network </term> or a <term> decision tree </term> .	#1162 We suggest a method that mimics the behavior of theoracle using a neural network or a decision tree.


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