Automated Template Extraction
Team Member(s)
- Francis Keith
- Andrew Rodriguez
- Anyone else who may be interested
Proposal
Template-based information extraction methods have one glaring weakness: they rely on - you guessed it - templates. These templates are often hand-crafted, and thus either require a significant amount of time and painstaking tuning, or they are prone to errors. Neither of these alternatives is ideal, so it would be beneficial if we could automatically produce these templates from data.
The paper referenced below by Chambers and Jurafsky is what we plan to use as a "jumping-off" point, so to speak.
We'd like to look more into the paper's methodology, apply it to a new domain, and potentially improve upon some methodology that is used.
Baseline & Dataset
(We're still a little bit unsure about this)
The Chambers and Jurafsky paper uses the MUC 4 data set on terrorism. We could use any of the MUC datasets. General MUC dataset information. Another possibility would be to show the power of extracting templates automatically by expanding it to work on a non-standard IE dataset.
In terms of a baseline, the methodology from the Chambers and Jurafsky is a good start, but it will depend on what dataset we'll choose to use. If we use MUC 4 and decide to improve upon the methodology around that dataset, then the baseline from Chambers and Jurafsky will be sufficient. The other option is to use a different dataset, in which case we'll use some "standard" template-based IE methods (admittedly, we haven't yet narrowed down what those methods will be)
Related Work
- Template-Based Information Extraction without the Templates by Nathanael Chambers and Dan Jurafsky
