Title: Computational Models of Discourse Analysis
1Computational Models of Discourse Analysis
- Carolyn Penstein Rosé
- Language Technologies Institute/
- Human-Computer Interaction Institute
2Pre-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!
3Remember that one of the instructional goals
of this course is to teach you how to read this
literature.
4Warm 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 dont work? How would the approach
need to be extended?
5Unit 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
6What 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).
7Dependency Relations
What is the potential downside of using
dependency relations as features?
8Why its tricky
9Why 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
10Possibly 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 Wiebes work
- Does this seem reasonable? Why or why not?
- Consistent with Appraisal theory?
11Student 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.
12Annotation
13Is 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?
14Alternative 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.
15Results
What questions are left unanswered and what
follow up experiments would you do? What ideas
does this paper give you for Assignment 3?
16Tips for Mondays 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 dont 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?
17Questions?