| other,7-2-E06-1022,bq | First , we investigate how well the <term> | addressee | </term> of a <term> dialogue act </term> can | #10260 First, we investigate how well theaddressee of a dialogue act can be predicted based on gaze, utterance and conversational context features. |
| tech,11-3-E06-1022,bq | <term> meeting context </term> can aid <term> | classifiers | </term> ' <term> performances </term> . Both <term> | #10289 Then, we explore whether information about meeting context can aidclassifiers' performances. |
| other,13-3-E06-1022,bq | </term> can aid <term> classifiers </term> ' <term> | performances | </term> . Both <term> classifiers </term> perform | #10291 Then, we explore whether information about meeting context can aid classifiers'performances. |
| other,14-4-E06-1022,bq | utterance features </term> are combined with <term> | speaker 's gaze information | </term> . The <term> classifiers </term> show | #10307 Both classifiers perform the best when conversational context and utterance features are combined withspeaker 's gaze information. |
| other,9-4-E06-1022,bq | <term> conversational context </term> and <term> | utterance features | </term> are combined with <term> speaker 's | #10302 Both classifiers perform the best when conversational context andutterance features are combined with speaker's gaze information. |
| tech,1-5-E06-1022,bq | speaker 's gaze information </term> . The <term> | classifiers | </term> show little <term> gain </term> from | #10313 Theclassifiers show little gain from information about meeting context. |
| other,4-5-E06-1022,bq | <term> classifiers </term> show little <term> | gain | </term> from information about <term> meeting | #10316 The classifiers show littlegain from information about meeting context. |
| other,7-1-E06-1022,bq | <term> addressee identification </term> in <term> | four-participants face-to-face meetings | </term> using <term> Bayesian Network </term> | #10242 We present results on addressee identification infour-participants face-to-face meetings using Bayesian Network and Naive Bayes classifiers. |
| other,17-2-E06-1022,bq | act </term> can be predicted based on <term> | gaze | </term> , <term> utterance </term> and <term> conversational | #10270 First, we investigate how well the addressee of a dialogue act can be predicted based ongaze, utterance and conversational context features. |
| other,7-3-E06-1022,bq | explore whether information about <term> | meeting context | </term> can aid <term> classifiers </term> ' <term> | #10285 Then, we explore whether information aboutmeeting context can aid classifiers' performances. |
| tech,14-1-E06-1022,bq | using <term> Bayesian Network </term> and <term> | Naive Bayes classifiers | </term> . First , we investigate how well | #10249 We present results on addressee identification in four-participants face-to-face meetings using Bayesian Network andNaive Bayes classifiers. |
| other,19-2-E06-1022,bq | predicted based on <term> gaze </term> , <term> | utterance | </term> and <term> conversational context features | #10272 First, we investigate how well the addressee of a dialogue act can be predicted based on gaze,utterance and conversational context features. |
| other,21-2-E06-1022,bq | gaze </term> , <term> utterance </term> and <term> | conversational context features | </term> . Then , we explore whether information | #10274 First, we investigate how well the addressee of a dialogue act can be predicted based on gaze, utterance andconversational context features. |
| tech,11-1-E06-1022,bq | face-to-face meetings </term> using <term> | Bayesian Network | </term> and <term> Naive Bayes classifiers </term> | #10246 We present results on addressee identification in four-participants face-to-face meetings usingBayesian Network and Naive Bayes classifiers. |
| tech,4-1-E06-1022,bq | algorithm </term> . We present results on <term> | addressee identification | </term> in <term> four-participants face-to-face | #10239 We present results onaddressee identification in four-participants face-to-face meetings using Bayesian Network and Naive Bayes classifiers. |
| tech,1-4-E06-1022,bq | </term> ' <term> performances </term> . Both <term> | classifiers | </term> perform the best when <term> conversational | #10294 Bothclassifiers perform the best when conversational context and utterance features are combined with speaker's gaze information. |
| other,10-2-E06-1022,bq | well the <term> addressee </term> of a <term> | dialogue act | </term> can be predicted based on <term> gaze | #10263 First, we investigate how well the addressee of adialogue act can be predicted based on gaze, utterance and conversational context features. |
| other,8-5-E06-1022,bq | <term> gain </term> from information about <term> | meeting context | </term> . Most state-of-the-art <term> evaluation | #10320 The classifiers show little gain from information aboutmeeting context. |
| other,6-4-E06-1022,bq | classifiers </term> perform the best when <term> | conversational context | </term> and <term> utterance features </term> | #10299 Both classifiers perform the best whenconversational context and utterance features are combined with speaker's gaze information. |
