Template-Based Information Extraction without the Templates by Nathanael Chambers and Dan Jurafsky.
Abstract:
Standard algorithms for template-based information extraction (IE) require predefined template schemas, and often labeled data, to learn to extract their slot fillers (e.g., an embassy is the Target of a Bombing template). This paper describes an approach to template-based IE that removes this requirement and performs extraction without knowing the template structure in advance. Our algorithm instead learns the template structure automatically from raw text, inducing template schemas as sets of linked events (e.g., bombings include detonate, set off, and destroy events) associated with semantic roles. We also solve the standard IE task, using the induced syntactic patterns to extract role fillers from specific documents. We evaluate on the MUC-4 terrorism dataset and show that we induce template structure very similar to hand-created gold structure, and we extract role fillers with an F1 score of .40, approaching the performance of algorithms that require full knowledge of the templates.
Can you say association?
Definitely points towards a pipeline approach to topic map authoring. To abuse the term, perhaps a “dashboard” that allows selection of data sources followed by the construction of workflows with preliminary analysis being displayed at “breakpoints” in the processing. No particular reason why stages have to be wired together other than tradition.
Just looking a little bit into the future, imagine that some entities weren’t being recognized at a high enough rate. So you shift that part of the data to several thousand human entity processors and take the average of their results, higher than what you were getting and feed that back into the system. Could have knowledge workers who work full time but shift from job to job performing tasks too difficult to program effectively.