Computational Models of Discourse Analysis

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

2. Pre-WarmUp Discussion • What can we do about jargon? • This paper for Wednesday is so jargon-ridden, I'm not sure if it actually makes sense or not. An example: "Essentially we find the transitive closure of the coreference and meronymy relations on the initial set of mentions" (first page, second column end of first full paragraph). ...and this is before any of the technical details!

3. Remember that one of the instructional goals of this course is to teach you how to read this literature.

4. Warm Up Discussion • How comprehensive is this table when we consider sentiment expressions and targets in our Appraisal theory analysis? • Look at the examples in the table and identify whether one of the paths would link the sentiment expression to its target. • Which ones don’t work? How would the approach need to be extended?

5. Unit 3 Plan • 3 papers we will discuss all give ideas for using context (at different grain sizes) • Local patterns without syntax • Using bootstrapping • Local patterns with syntax • Using a parser • Rhetorical patterns within documents • Using a statistical modeling technique • The first two papers introduce techniques that could feasibly be used in your Unit 3 assignment

6. What can be evaluated? • Also, from the definition, it seems that 'mentions' are just any noun or possessive pronoun (or features of these that can be evaluated). I guess these are the only things that can be evaluated, although I'm not sure of the possessive pronouns (my, its, his, etc).

7. Dependency Relations What is the potential downside of using dependency relations as features?

8. Why it’s tricky…

9. Why dependency relations are important for sentiment • A big candy bar versus a big nose • A deep thought versus a deep hole • Hard wood floor versus hard luck • Cold drink versus cold hamburger • Furry cat versus furry food • Ancient wisdom versus ancient hardware

10. Possibly unintuitive attributions • What sentiment is expressed by this sentence: • I broke the handle • They argue that the speaker expresses regret about his own actions • Comes from Wilson and Wiebe’s work • Does this seem reasonable? Why or why not? • Consistent with Appraisal theory?

11. Student Comment • I think, like suggestions for the other paper, this paper could possibly include the positive/negative dimension of Appraisal Theory, but I'm not sure how often these situations actually come up. Example (7) on page 96 shows one example, but I'm not sure if this genre of ambiguity is common.

12. Annotation

13. Is there a problem here? • Explain how this sentiment propagation graph would be used in sentiment analysis. • Can you see a problem that would occur if you apply this to movie reviews? What slight modification fixes the problem?

14. Alternative Approaches • Proximity: pick the closest target • Heuristic Syntax: shortest path • Bloom: hand crafted dependency paths • RankSVM: learn weights on types of evidence for ranking targets Not clear how much advantage from types of features versus the supervised learning approach.

15. Results From Table 5, I'm not entirely sure how to interpret what is a "good" result (with respect to # correct targets, precision, and possibly F-score). Basically, if it's not a Kappa value (i.e. .70 or higher), than which thresholds must be met to be 'okay' or 'good.' What questions are left unanswered and what follow up experiments would you do? What ideas does this paper give you for Assignment 3?

16. Tips for Monday’s Reading Assignment • Skip Section 4 and the Appendix the first time you read the paper • Then skim through section 4, skipping over any sentences you don’t understand • Focus on the initial paragraphs in sections/subsections, as these tend to give a high level idea of what the message is • Keep in mind that their Latent Sentence Perspective Model is just Naïve Bayes with one twist – can you find what that one twist is?

17. Questions?