CAREER: Learning Coherent Concepts: Theory and Applications to Natural Language

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This is the first year of funding of a 4-year continuing award. This research investigates learning scenarios in which multiple learners co-exist and may learn different functions on the same input, but there are mutual compatibility constraints on their outcomes. The PI will develop a learning theory for these situations, and will study algorithmic ways to exploit them in natural language inference. The theoretical study will concentrate, on developing a semantics for the coherency conditions and study it from a learning theory point of view with a goal of understanding in what ways learning becomes easier and more robust in these situations. The algorithmic study will concentrate on developing ways to exploit coherency and will have a significant experimental component, using the problem of shallow parsing as a testbed for investigating chaining of coherent classifiers and inferences that rely on the outcomes of several classifiers This research will have a significant impact on theoretical research in learning and on our ability to perform higher level inference in natural language. It will help to resolve the contrast between the predicted hardness of learning and the apparent ease at which cognitive systems learn. Moreover, it will provide an understanding of how to exploit coherency in order to develop better learning and inference methods for these situations, and will result in an integrated learning approach to a variety of shallow parsing tasks, implemented and demonstrated in a large scale manner using the SNoW learning architecture. Incorporating the understanding of interacting classifiers, as well as methods to perform inferences that rely on several classifiers, into SNoW will be directly applicable to a variety of other tasks in this domain. http://L2R.cs.uiuc.edu/~danr/Grants/eareerOO.html