learning argumentative structures

[gusl]

unified architectures for Machine Learning?

Our ontology is, as usual: observables (inputs) and unobservables we are interested in (output).

The purpose of much machine learning is to: given some data, induce a function that, when given a new data point with partial information, will let us complete it.



Analogy: reconstructing an image

Supervised learning is when we learn from complete images, and then perform the task of filling in the missing area.
Unsupervised learning is when we learn from incomplete images to begin with. The goal may be to complete the square, or merely to find a classification of the incomplete images.

But in a more general context, the different variables associated with the data will have different units and types (e.g. two-valued, multi-valued, discrete, continuous, etc). While such data points can still be encoded as images (everything can), we lose contraints that made learning feasible (e.g. continuity).

My impression is that many learning systems are hard-coded for a specific learning function (i.e. a given set of inputs and outputs), and aren't robust to changes, i.e. if you add an input the system won't improve, if you remove an input the system breaks. If we have a system that has learned an estimate of 1=>2, it should be easy to turn that learning into an estimate of 2=>1 (of course, if 1=>2 is very information-lossy, your standard for 2=>1 can't be very high).

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Learning argumentative structures

Anyway, I mention all this because in my current project on learning argumentative structures, our most ambitious goal (i.e. automating the process of argument-mapping) involves a multi-step process, and we may or may not want to add scaffolding (different levels of human-annotation) along the way.

Our "variables":

1 skeleton of the graph
2 text in nodes
3 raw source text
4 text segments ("quotes") to be used
5 links between text segments and nodes

The ambitious goal is to learn 3 => 1,2,5 (4 being a necessary intermediate step)

2 => 4 should be easy, as the text in nodes tends to be close paraphrases of the source, and the target space is small (there are only so many quotes you can take a short text).
1,3,4 => 5 should be easy to make perform reasonably well by using simple heuristics about ordering and textual cues (words like "therefore").
1,3 => 2 could benefit from this heuristic: if you see the text "we assume that" in 3, the sentence following that must be a leaf node (i.e. axiom node). Likewise, some cues may help us identify the root node: maybe "therefore" gets used in final conclusions whereas "thus" is used in intermediate nodes more often.
1,2,3,4 => 5 is easy: 1,3,4 => 5 is feasible already, and now we have 2, which makes the problem almost trivial: just use string matching.

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Collecting data:

fixed text, different graphs: read & formalize assignments
fixed graph, different texts: read graph & write assigments (expose the author's point of view)

Comments

[gustavolacerda.livejournal.com]

It's my own very uninformed impression. What I have in mind is NLP systems.