Computational Models of Discourse Analysis Carolyn Penstein Rosé Language Technologies Institute/ Human-Computer Interaction Institute

Warm Up Discussion What different perspectives were represented in the review? What would you say is the one sentence summary of the review author’s appraisal of the book? What evidence do you draw on to make that conclusion? Where do the Appraisal tags fit in? Do you find “distractors” in the text that would make it difficult computationally to find the point?

Reflections on Assignment 2 What connection did you see between the theory we read and what you did? What was challenging about making a connection? What would you have liked to have done but were not able to?

Reflections on Assignment 2 Eric: I would have liked to test out some of my hypotheses on the various concepts that we learned about and applied simple rules in an informal manner on a moderately significant data set. This of course requires more time and effort all around. Elijah:I think it would be worthwhile to take a very small amount of data - measuring in sentences, not paragraphs or pages - and try to really formally lay it out in terms of frames, targets, attitudes, etc. All of the things that we studied in the earlier weeks of this semester (well, not all of them, but at least a few) can be used as inspiration for how to model all of these different things that Attitude does for us

Reflections on Assignment 2 Beka: Okay, so for Assignment 2, I felt that although the readings were connected to the assignment, the data we had to work with had a complete different level of detail/set of information than what we'd seen on the paper. This is not really a bad thing, since this was a hw assignment and not a research project but most of what we saw in the papers weren't techniques we could apply. Generally, I'd like to suggest that this web page have a homework page where we can find a description of the assignment and information about where to find the files we're supposed to analyze. Iris ending up sending me the MapTask files because my version of SIDE didn't have them (maybe it's too old?). Morals of this story: a) Thanks, Iris! b) it can be difficult to find this information on the slides/remember it from lecture.

Reflections on Assignment 2 As for the assignment, I wasted a considerable amount of time trying to do things that the system could not do (although, may someday do, when Elijah fixes all the kinks). This was the most frustrating part. Once I figured out the simplified process that worked, I was actually able to focus on the assignment itself. I didn't have any problems connecting the hands-on assignment with readings, I don't think...although a lot of the technical readings appear way more complicated than what we actually did.

Assignment 3 (not due til Mar23) Look at the Movie Review dataset and polarity coding that is provided Think about what distinguishes the codes at a linguistic level Do an error analysis on the dataset using a simple unigram baseline, and from that propose one or a few new types of features motivated by your linguistic understanding of Affect Due on Week 11 lecture 2 Turn in your feature extractors (documented code) or notes about your approach Have a 5 minute powerpoint presentation ready for class on Week 11 lecture 2

What would you say appraisal is doing in text? 3 types: Affect (what we feel), Judgment (values ascribed to behavior), Appreciation (values ascribed to entities) David commented that appreciation is hard to distinguish from everything else. Others agree or disagree?

Chicken and Egg… Main issue for this week: Exploring attitude What should we take from an appraisal? And how do we find it? Operationalization Computationalization * Here we’ll see our problem is more complicated since the text is structured at multiple levels. Forward pointer to the Engagement framework…

Strategies Where do you see these in the book review? Do we see signposts that tell us how to interpret invoked appraisals?

What’s tricky?

What’s tricky?

Questions?