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measure(ment),15-2-H01-1058,bq	interpolation </term> , improve the <term>	performance	</term> but fall short of the <term> performance	#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),15-3-H01-1058,bq	<term> word string </term> with the best <term>	performance	</term> ( typically , <term> word or semantic	#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),18-5-H01-1058,bq	model combination </term> to improve the <term>	performance	</term> further . We suggest a method that	#1149 We provide experimental results that clearly show the need for a dynamic language model combination to improve theperformance further.
measure(ment),19-3-H01-1058,bq	<term> performance </term> ( typically , <term>	word or semantic error rate	</term> ) from a list of <term> word strings	#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.
measure(ment),21-2-H01-1058,bq	performance </term> but fall short of the <term>	performance	</term> of an <term> oracle </term> . The <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.
measure(ment),21-7-H01-1058,bq	to the <term> LM </term> with the best <term>	confidence	</term> . We describe a three-tiered approach	#1193 The method amounts to tagging LMs with confidence measures and picking the best hypothesis corresponding to the LM with the bestconfidence.
measure(ment),7-7-H01-1058,bq	amounts to tagging <term> LMs </term> with <term>	confidence measures	</term> and picking the best <term> hypothesis	#1179 The method amounts to tagging LMs withconfidence measures and picking the best hypothesis corresponding to the LM with the best confidence.
model,11-1-H01-1058,bq	address the problem of combining several <term>	language models ( LMs )	</term> . We find that simple <term> interpolation	#1038 In this paper, we address the problem of combining severallanguage models ( LMs ).
model,17-7-H01-1058,bq	hypothesis </term> corresponding to the <term>	LM	</term> with the best <term> confidence </term>	#1189 The method amounts to tagging LMs with confidence measures and picking the best hypothesis corresponding to theLM with the best confidence.
model,43-3-H01-1058,bq	been obtained by using a different <term>	LM	</term> . Actually , the <term> oracle </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.
model,5-7-H01-1058,bq	</term> . The method amounts to tagging <term>	LMs	</term> with <term> confidence measures </term>	#1177 The method amounts to taggingLMs with confidence measures and picking the best hypothesis corresponding to the LM with the best confidence.
other,1-3-H01-1058,bq	</term> of an <term> oracle </term> . The <term>	oracle	</term> knows the <term> reference word string	#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,10-3-H01-1058,bq	word string </term> and selects the <term>	word string	</term> with the best <term> performance </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.
other,10-4-H01-1058,bq	a <term> dynamic combiner </term> with <term>	hard decisions	</term> using the <term> reference </term> .	#1125 Actually, the oracle acts like a dynamic combiner withhard decisions using the reference.
other,10-6-H01-1058,bq	method that mimics the behavior of the <term>	oracle	</term> using a <term> neural network </term>	#1162 We suggest a method that mimics the behavior of theoracle using a neural network or a decision tree.
other,13-7-H01-1058,bq	measures </term> and picking the best <term>	hypothesis	</term> corresponding to the <term> LM </term>	#1185 The method amounts to tagging LMs with confidence measures and picking the besthypothesis corresponding to the LM with the best confidence.
other,14-4-H01-1058,bq	<term> hard decisions </term> using the <term>	reference	</term> . We provide experimental results	#1129 Actually, the oracle acts like a dynamic combiner with hard decisions using thereference.
other,24-2-H01-1058,bq	of the <term> performance </term> of an <term>	oracle	</term> . The <term> oracle </term> knows the	#1068 We find that simple interpolation methods, like log-linear and linear interpolation, improve the performance but fall short of the performance of anoracle.
other,29-3-H01-1058,bq	error rate </term> ) from a list of <term>	word strings	</term> , where each <term> word string </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,3-4-H01-1058,bq	different <term> LM </term> . Actually , the <term>	oracle	</term> acts like a <term> dynamic combiner	#1118 Actually, theoracle acts like a dynamic combiner with hard decisions using the reference.


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