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Computational Models of Discourse Analysis

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

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Computational Models of Discourse Analysis

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

  2. Warm-Up Discussion • There definitely is a content difference between male and female blogs in this corpus • The question is whether there are also stylistic differences • Typical findings from genre analysis: women hedge more, use more indicators of involvement, and speak less formally • These patterns are known to be detectable through POS n-grams • But is there something for Engagement here? • If women show more involvement, wouldn’t you expect it to show up in Engagement like features? • What would you look for? 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?

  3. Now read the excerpt from the Herring article Does that change your view at all on the extent to which Engagement is relevant?

  4. Question about Assignment • I also think that some sort of bootstrapping may be useful for finding clusters of related words, and this time we have extra data that we could use. Would it be acceptable to use this data in the unlabelled form? • Yes!

  5. Can you explain the difference between these three sentences: • They claimed that he is a stellar PhD student. • They stated that he is a stellar PhD student. • They demonstrated that he is a stellar PhD student.

  6. How is “really” functioning in this passage, and what does that mean for Engagement oriented feature extraction?

  7. Heteroglossia vs. Hedging * What is your definition of a hedge? Is hedging the only function of Engagement?

  8. Engagement • Already established: Positioning a proposition • But can it also be primarily positioning between people? • Patterns of positioning propositions as having the same or different alignment between speaker and hearer could do this • Is positioning in communication always positioning by means of propositional content?

  9. Gee vs. SFL • Heteroglossia is like a tapestry • Gee was referring to the individual colored threads being woven together • The subtance of the perspective • Martin and White are referring to what holds the threads together • More focus on alignment versus disalignment What happens when you borrow other people’s words (Jim Gee’s heteroglossia) but present them without markers of alignment or disalignment (SFL monoglossia)?

  10. Connection between Heteroglossia and Attitude But is this really different from a disclaim? And is this really different from a proclaim?

  11. Student Comment • I agree with what David said in class on Monday--that heterogloss, as defined in the reading, sounds much more like hedging than what Gee's and what I imagine Bakhtin's concepts of it were. And as such, I believe hedging is a much more effective tool in showing generational or occupational differences rather than gender differences.

  12. Accommodation 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.

  13. Other views of positioning • From Tannen’s Framing in Discourse • Words that sound like a role • The substance of that role – in the Gee sense • In this case, speaking the words shows alignment because you use them as though they are your own

  14. Connections • Alignment and Solidarity (positioning between people) • Can you think of other indicators of solidarity • Personal pronouns • Accommodation • Friendly language • Factivity (positioning between people and propositions) • I regret eating that chocolate • Is this heteroglossic?

  15. How would an Engagement style analysis compare/relate to the LIWC analysis we discussed last time?

  16. Student Comment • Iris: I think that not only may it sound sexist to hypothesize about what people of a certain gender talk like but also may the way people talk be more indicative of where they are from.

  17. How would you expect an Engagement style analysis to relate to personality? • What effect would you expect to see on conversations? • Are these necessarily connected?

  18. Freshman Engineering Study • 131 Freshman engineering students worked in groups of 3 or 4 to design a better wrench • Applying principles related to stress and leverage • Procedure • Tutorial on computer aided engineering • Pretest • Collaborative design activity • Posttest • Questionnaire

  19. Heteroglossia Manipulation

  20. Social Manipulation

  21. ConcertChat Server ConcertChatActor ConcertChatListener MessageFilter PresenceFilter DiscourseMemory AnnotationFilter OutputCoordinator SocialController ActivityDetector ProgressDetector PlanExecutor RequestDetector T.TakingCoordinator IntroductionsManager PromptingManager TutoringManager TutoringActor IntroductionsActor PromptingActor Tutor 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

  22. Results on Breadth of Coverage of Design Space • Significant main effect of Heteroglossia on number of ideas mentioned • Heteroglossia was better than Monoglossia and Neutral • Significant interaction • In the Social condition, Monoglossia was worse than the other two

  23. Results on Perception • Students were significantly happier with the interaction in the Heteroglossia condition than Neutral, with Monoglossia in the middle • Students liked the Heteroglossic and Monoglossic agents better than the Neutral agent • Students in the Heteroglossia condition felt marginally more successful than students in the Monoglossia condition • No effect on Personality indicators such as Pushy, Wishy Washy, etc. • Does that mean that impression of personality and how you feel about an interaction with someone are not linked?

  24. Student Comment • I would also note that English is a very gender neutral language, so gender performativity is harder to classify.

  25. Discussion from Last Time • How would you achieve a good balance between an operationalization of Engagement that is useful and yet attainable in terms of reliability of coding?

  26. Hedging and Occupation? • And as such, I believe hedging is a much more effective tool in showing generational or occupational differences rather than gender differences. • For example, teenagers often use verbs such as 'like' and 'all' to report speech: he was all 'that's stupid' and then he was like ''but I'm stupid too'. The occupational differences I would attribute to the differences between people who need exact values as opposed to people who can accept generalizations or approximations.

  27. Questions?

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