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