Gustavo Lacerda

I finally have a concrete plan for my "learning argumentative structures" project.

(1) Make a corpus from TruthMapping.com and from iLogos logs, from arguments that have short boxes and use a formalistic style.
(1.1) Ask my sources for XML files containing the raw arguments.
(1.2) Annotate each inference (triangle) as: WTF!, Modus Ponens, Modus Tollens, UI-MP, UI-MT, EI, and some commonly-occurring fallacies.
*: I'm not sure what to do when the inference step is not a triangle (i.e. #premises!=2). At first pass I intend to just ignore them.
(2) Train a classifier with this annotated corpus. Triangle-shaped subtrees are positive examples. As negative examples, I could use triangles randomly-generated from other boxes in the argument.
(3) Use this to judge when a triangle is valid (and, in particular, what kind of inference it is). Evaluate using cross-validation.

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Building on this idea: we may have a corpus of an argument-diagramming task, showing the source text and the diagram next to each other.
(1) Annotate it by linking (this can probably be done automatically, by text similarity)
(2) Learn how to roughly perform this task automatically (the text in the boxes is likely to be clunky).
(2.1) Evaluate: do automatically-generated diagrams correspond closely to human-made diagrams?
(3) Using the classifier above, automatically check whether the inferences in the generated map are valid. This is a judgement of the validity of the argument presented in textual form.
(4) New task for the purposes of evaluating the combined algorithms: formalize a text, and judge the validity of the inferences. Does the computer's performance correspond closely to human performance?

See http://www.optimizelife.com/wiki/index.php/My_Breathing#Causal_Processes

All the pieces finally fall into place:

Causal diagram of Chronic Rhinitis

Solid lines mean positive influence (+), i.e. more of the source tends to cause more of the target.
Dashed lines mean negative influence (-), i.e. more of the source tends to cause less of the target.


N.B.: I don't suffer from all causes or all symptoms above.

I could add a node for "vasoconstrictor" (e.g. Afrin) right next to "fluticasone", having a negative (e.g. health-positive) effect on "amount of blood in mucosa", but the problem is that vasoconstrictors have a short-term effect that rebound, becoming a positive (e.g. health-negative) effect.

Thanks WikiTex/Wikisophia, for providing me with a sandbox! Wiki code is behind the cut.

Fluticasone appears to be effective in the long run. But if I end up needing to use it for the rest of my life, then I'll go for a ~50% partial turbinectomy (under the knife, since laser seems to damage mucociliary function).

I am interested in the semantics of these diagrams, and how they relate to argument maps and formal proofs.

semantics of diagrams

* Say we want to instantiate a particular allergen and a particular individual: what kind of graph rewriting will we need to do?

* What about expressing the distinction between independent and dependent influences (e.g. conjunction, synergy)?

* What about tagging nodes with information about which leaves are controllable?

* Some effects have preconditions: snoring requires sleeping. Sleeping requires lying down. So we have an implicit relationship in the graph: the consequence is that turbinate enlargement will be worse during sleep. Could conclusions of the kind be drawn automatically, by simply adding to the implicit information to the current representation?


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