Concordance

Query P03-1033 27 (1,258.8 per million)

tech,3-1-P03-1033,bq	We address appropriate <term> user modeling </term> in order to generate <term> cooperative responses </term> to each <term> user </term> in <term> spoken dialogue systems </term> .	#4282 We address appropriateuser modeling in order to generate cooperative responses to each user in spoken dialogue systems.
other,9-1-P03-1033,bq	We address appropriate <term> user modeling </term> in order to generate <term> cooperative responses </term> to each <term> user </term> in <term> spoken dialogue systems </term> .	#4288 We address appropriate user modeling in order to generatecooperative responses to each user in spoken dialogue systems.
other,13-1-P03-1033,bq	We address appropriate <term> user modeling </term> in order to generate <term> cooperative responses </term> to each <term> user </term> in <term> spoken dialogue systems </term> .	#4292 We address appropriate user modeling in order to generate cooperative responses to eachuser in spoken dialogue systems.
tech,15-1-P03-1033,bq	We address appropriate <term> user modeling </term> in order to generate <term> cooperative responses </term> to each <term> user </term> in <term> spoken dialogue systems </term> .	#4294 We address appropriate user modeling in order to generate cooperative responses to each user inspoken dialogue systems.
other,6-2-P03-1033,bq	Unlike previous studies that focus on <term> user </term> 's <term> knowledge </term> or typical kinds of <term> users </term> , the <term> user model </term> we propose is more comprehensive .	#4304 Unlike previous studies that focus onuser's knowledge or typical kinds of users, the user model we propose is more comprehensive.
other,8-2-P03-1033,bq	Unlike previous studies that focus on <term> user </term> 's <term> knowledge </term> or typical kinds of <term> users </term> , the <term> user model </term> we propose is more comprehensive .	#4306 Unlike previous studies that focus on user'sknowledge or typical kinds of users, the user model we propose is more comprehensive.
other,13-2-P03-1033,bq	Unlike previous studies that focus on <term> user </term> 's <term> knowledge </term> or typical kinds of <term> users </term> , the <term> user model </term> we propose is more comprehensive .	#4311 Unlike previous studies that focus on user's knowledge or typical kinds ofusers, the user model we propose is more comprehensive.
model,16-2-P03-1033,bq	Unlike previous studies that focus on <term> user </term> 's <term> knowledge </term> or typical kinds of <term> users </term> , the <term> user model </term> we propose is more comprehensive .	#4314 Unlike previous studies that focus on user's knowledge or typical kinds of users, theuser model we propose is more comprehensive.
model,8-3-P03-1033,bq	Specifically , we set up three dimensions of <term> user models </term> : <term> skill level </term> to the <term> system </term> , <term> knowledge level </term> on the <term> target domain </term> and the degree of <term> hastiness </term> .	#4330 Specifically, we set up three dimensions ofuser models: skill level to the system, knowledge level on the target domain and the degree of hastiness.
other,11-3-P03-1033,bq	Specifically , we set up three dimensions of <term> user models </term> : <term> skill level </term> to the <term> system </term> , <term> knowledge level </term> on the <term> target domain </term> and the degree of <term> hastiness </term> .	#4333 Specifically, we set up three dimensions of user models:skill level to the system, knowledge level on the target domain and the degree of hastiness.
tech,15-3-P03-1033,bq	Specifically , we set up three dimensions of <term> user models </term> : <term> skill level </term> to the <term> system </term> , <term> knowledge level </term> on the <term> target domain </term> and the degree of <term> hastiness </term> .	#4337 Specifically, we set up three dimensions of user models: skill level to thesystem, knowledge level on the target domain and the degree of hastiness.
other,17-3-P03-1033,bq	Specifically , we set up three dimensions of <term> user models </term> : <term> skill level </term> to the <term> system </term> , <term> knowledge level </term> on the <term> target domain </term> and the degree of <term> hastiness </term> .	#4339 Specifically, we set up three dimensions of user models: skill level to the system,knowledge level on the target domain and the degree of hastiness.
other,21-3-P03-1033,bq	Specifically , we set up three dimensions of <term> user models </term> : <term> skill level </term> to the <term> system </term> , <term> knowledge level </term> on the <term> target domain </term> and the degree of <term> hastiness </term> .	#4343 Specifically, we set up three dimensions of user models: skill level to the system, knowledge level on thetarget domain and the degree of hastiness.
other,27-3-P03-1033,bq	Specifically , we set up three dimensions of <term> user models </term> : <term> skill level </term> to the <term> system </term> , <term> knowledge level </term> on the <term> target domain </term> and the degree of <term> hastiness </term> .	#4349 Specifically, we set up three dimensions of user models: skill level to the system, knowledge level on the target domain and the degree ofhastiness.
model,3-4-P03-1033,bq	Moreover , the <term> models </term> are automatically derived by <term> decision tree learning </term> using real <term> dialogue data </term> collected by the <term> system </term> .	#4354 Moreover, themodels are automatically derived by decision tree learning using real dialogue data collected by the system.
tech,8-4-P03-1033,bq	Moreover , the <term> models </term> are automatically derived by <term> decision tree learning </term> using real <term> dialogue data </term> collected by the <term> system </term> .	#4359 Moreover, the models are automatically derived bydecision tree learning using real dialogue data collected by the system.
lr,13-4-P03-1033,bq	Moreover , the <term> models </term> are automatically derived by <term> decision tree learning </term> using real <term> dialogue data </term> collected by the <term> system </term> .	#4364 Moreover, the models are automatically derived by decision tree learning using realdialogue data collected by the system.
tech,18-4-P03-1033,bq	Moreover , the <term> models </term> are automatically derived by <term> decision tree learning </term> using real <term> dialogue data </term> collected by the <term> system </term> .	#4369 Moreover, the models are automatically derived by decision tree learning using real dialogue data collected by thesystem.
measure(ment),3-5-P03-1033,bq	We obtained reasonable <term> classification accuracy </term> for all dimensions .	#4374 We obtained reasonableclassification accuracy for all dimensions.
other,0-6-P03-1033,bq	We obtained reasonable <term> classification accuracy </term> for all dimensions . <term> Dialogue strategies </term> based on the <term> user modeling </term> are implemented in <term> Kyoto city bus information system </term> that has been developed at our laboratory .	#4380 We obtained reasonable classification accuracy for all dimensions.Dialogue strategies based on the user modeling are implemented in Kyoto city bus information system that has been developed at our laboratory.


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