| other,16-2-N03-2003,ak | supplemented with <term> text </term> from the <term> | web | </term> filtered to match the <term> style </term> | #3045 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. | |
| tech,46-2-N03-2003,ak | from the <term> data </term> by using <term> | class-dependent interpolation | </term> of <term> N-grams </term> . In order | #3075 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. | |
| tech,6-1-N03-2003,ak | <term> training data </term> suitable for <term> | language modeling | </term> of <term> conversational speech </term> | #3021 Sources of training data suitable forlanguage modeling of conversational speech are limited. | |
| other,9-1-N03-2003,ak | for <term> language modeling </term> of <term> | conversational speech | </term> are limited . In this paper , we | #3024 Sources of training data suitable for language modeling ofconversational speech are limited. | |
| lr,2-1-N03-2003,ak | tagging result </term> . Sources of <term> | training data | </term> suitable for <term> language modeling | #3017 Sources oftraining data suitable for language modeling of conversational speech are limited. | |
| other,21-2-N03-2003,ak | <term> web </term> filtered to match the <term> | style | </term> and/or <term> topic </term> of the <term> | #3050 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. | |
| lr,7-2-N03-2003,ak | limited . In this paper , we show how <term> | training data | </term> can be supplemented with <term> text | #3036 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. | |
| lr,43-2-N03-2003,ak | bigger performance gains from the <term> | data | </term> by using <term> class-dependent interpolation | #3072 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. | |
| other,23-2-N03-2003,ak | match the <term> style </term> and/or <term> | topic | </term> of the <term> target recognition task | #3052 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,26-2-N03-2003,ak | </term> and/or <term> topic </term> of the <term> | target 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 thetarget 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,ak | data </term> can be supplemented with <term> | text | </term> from the <term> web </term> filtered | #3042 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,49-2-N03-2003,ak | class-dependent interpolation </term> of <term> | N-grams | </term> . In order to boost the <term> translation | #3078 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. |
