| other,11-3-E06-1022,ak | <term> meeting context </term> can aid <term> | classifiers ' performances | </term> . Both <term> classifiers </term> perform | #11226 Then, we explore whether information about meeting context can aidclassifiers ' performances. | |
| tech,11-1-E06-1022,ak | four-participants face-to-face meetings using <term> | Bayesian Network and Naive Bayes classifiers | </term> . First , we investigate how well | #11183 We present results on addressee identification in four-participants face-to-face meetings usingBayesian Network and Naive Bayes classifiers. | |
| other,8-5-E06-1022,ak | little gain from information about <term> | meeting context | </term> . Most state-of-the-art <term> evaluation | #11257 The classifiers show little gain from information aboutmeeting context. | |
| other,14-4-E06-1022,ak | utterance features </term> are combined with <term> | speaker 's gaze information | </term> . The <term> classifiers </term> show | #11244 Both classifiers perform the best when conversational context and utterance features are combined withspeaker 's gaze information. | |
| other,17-2-E06-1022,ak | act </term> can be predicted based on <term> | gaze , utterance and conversational context features | </term> . Then , we explore whether information | #11207 First, we investigate how well the addressee of a dialogue act can be predicted based ongaze , utterance and conversational context features. | |
| other,6-4-E06-1022,ak | classifiers </term> perform the best when <term> | conversational context | </term> and <term> utterance features </term> | #11236 Both classifiers perform the best whenconversational context and utterance features are combined with speaker's gaze information. | |
| other,9-4-E06-1022,ak | <term> conversational context </term> and <term> | utterance features | </term> are combined with <term> speaker 's | #11239 Both classifiers perform the best when conversational context andutterance features are combined with speaker's gaze information. | |
| other,7-3-E06-1022,ak | explore whether information about <term> | meeting context | </term> can aid <term> classifiers ' performances | #11222 Then, we explore whether information aboutmeeting context can aid classifiers' performances. | |
| other,10-2-E06-1022,ak | well the <term> addressee </term> of a <term> | dialogue act | </term> can be predicted based on <term> gaze | #11200 First, we investigate how well the addressee of adialogue act can be predicted based on gaze, utterance and conversational context features. | |
| tech,4-1-E06-1022,ak | algorithm </term> . We present results on <term> | addressee identification | </term> in four-participants face-to-face | #11176 We present results onaddressee identification in four-participants face-to-face meetings using Bayesian Network and Naive Bayes classifiers. | |
| other,7-2-E06-1022,ak | First , we investigate how well the <term> | addressee | </term> of a <term> dialogue act </term> can | #11197 First, we investigate how well theaddressee of a dialogue act can be predicted based on gaze, utterance and conversational context features. | |
| tech,1-4-E06-1022,ak | classifiers ' performances </term> . Both <term> | classifiers | </term> perform the best when <term> conversational | #11231 Bothclassifiers perform the best when conversational context and utterance features are combined with speaker's gaze information. | |
| tech,1-5-E06-1022,ak | speaker 's gaze information </term> . The <term> | classifiers | </term> show little gain from information | #11250 Theclassifiers show little gain from information about meeting context. |
