Concordance

Query N03-2003 12 (559.5 per million)

lr,2-1-N03-2003,bq	<term> tagging </term> result . Sources of <term>	training data	</term> suitable for <term> language modeling	#3016 Sources oftraining data suitable for language modeling of conversational speech are limited.
tech,6-1-N03-2003,bq	<term> training data </term> suitable for <term>	language modeling	</term> of <term> conversational speech </term>	#3020 Sources of training data suitable forlanguage modeling of conversational speech are limited.
other,9-1-N03-2003,bq	for <term> language modeling </term> of <term>	conversational speech	</term> are limited . In this paper , we	#3023 Sources of training data suitable for language modeling ofconversational speech are limited.
lr,7-2-N03-2003,bq	limited . In this paper , we show how <term>	training data	</term> can be supplemented with <term> text	#3035 In this paper, we show howtraining data can be supplemented with text from the web filtered to match the style and/or topic of the target recognition task, but also that it is possible to get bigger performance gains from the data by using class-dependent interpolation of N-grams.
other,13-2-N03-2003,bq	data </term> can be supplemented with <term>	text	</term> from the <term> web </term> filtered	#3041 In this paper, we show how training data can be supplemented withtext from the web filtered to match the style and/or topic of the target recognition task, but also that it is possible to get bigger performance gains from the data by using class-dependent interpolation of N-grams.
other,16-2-N03-2003,bq	supplemented with <term> text </term> from the <term>	web	</term> filtered to match the <term> style </term>	#3044 In this paper, we show how training data can be supplemented with text from theweb filtered to match the style and/or topic of the target recognition task, but also that it is possible to get bigger performance gains from the data by using class-dependent interpolation of N-grams.
other,21-2-N03-2003,bq	<term> web </term> filtered to match the <term>	style	</term> and/or <term> topic </term> of the target	#3049 In this paper, we show how training data can be supplemented with text from the web filtered to match thestyle and/or topic of the target recognition task, but also that it is possible to get bigger performance gains from the data by using class-dependent interpolation of N-grams.
other,23-2-N03-2003,bq	match the <term> style </term> and/or <term>	topic	</term> of the target <term> recognition task	#3051 In this paper, we show how training data can be supplemented with text from the web filtered to match the style and/ortopic of the target recognition task, but also that it is possible to get bigger performance gains from the data by using class-dependent interpolation of N-grams.
tech,27-2-N03-2003,bq	and/or <term> topic </term> of the target <term>	recognition task	</term> , but also that it is possible to	#3055 In this paper, we show how training data can be supplemented with text from the web filtered to match the style and/or topic of the targetrecognition task, but also that it is possible to get bigger performance gains from the data by using class-dependent interpolation of N-grams.
lr,43-2-N03-2003,bq	bigger performance gains from the <term>	data	</term> by using <term> class-dependent interpolation	#3071 In this paper, we show how training data can be supplemented with text from the web filtered to match the style and/or topic of the target recognition task, but also that it is possible to get bigger performance gains from thedata by using class-dependent interpolation of N-grams.
tech,46-2-N03-2003,bq	from the <term> data </term> by using <term>	class-dependent interpolation	</term> of <term> N-grams </term> . In order	#3074 In this paper, we show how training data can be supplemented with text from the web filtered to match the style and/or topic of the target recognition task, but also that it is possible to get bigger performance gains from the data by usingclass-dependent interpolation of N-grams.
other,49-2-N03-2003,bq	class-dependent interpolation </term> of <term>	N-grams	</term> . In order to boost the <term> translation	#3077 In this paper, we show how training data can be supplemented with text from the web filtered to match the style and/or topic of the target recognition task, but also that it is possible to get bigger performance gains from the data by using class-dependent interpolation ofN-grams.


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