Computational Models of Discourse Analysis. Carolyn Penstein Ros é Language Technologies Institute/ Human-Computer Interaction Institute. Warm-Up Discussion. There definitely is a content difference between male and female blogs in this corpus
Carolyn Penstein Rosé
Language Technologies Institute/
Human-Computer Interaction Institute
Analogy with educational research:
Pretest score always accounts for most of the variance in posttest scores.
If you don’t control for pretest score (by using it as a covariate in your comparisons of posttest) you frequently can’t see a meaningful difference between conditions.
But when you do control for it, you frequently can.
It almost always explains much less variance than pretest score. However, you can still see large effect sizes related to other factors.
* What would you expect involvement to look like?
* What is your definition of a hedge? Is hedging the only function of Engagement?
What happens when you borrow other people’s words (Jim Gee’s heteroglossia) but present them without markers of alignment or disalignment (SFL monoglossia)?
But is this really different from a disclaim?
And is this really different from a proclaim?
by pointing out the inflationof Saddam’s body count by neoconsin an effort to further vilify him and thus further justify our invasion we are not DEFENDING saddam....just pointing out how neoconsrarely let facts get in the way of a good war.
So wait, how many do you think Saddam killed or oppressed? You’re trying to make him look better than he actually was. You’re the one inflatingthe casualties we’ve caused! Seriously, what estimates (with a link) are there that we’ve killed over 100,000 civilians. Not some crack pot geocities page either.
ConcertChat Server to personality?
PromptingActorTutor Agent Design
Kumar, R. & Rosé, C. P. (2011). Architecture for building Conversational Agents that support Collaborative Learning, IEEE Transactions on Learning Technologies special issue on Intelligent and Innovative Support Systems for Computer Supported Collaborative Learning