RI-Medium: Collaborative Research: Dynamically-Structured Conditional Random Fields for Complex, Natural Domains

University Of Massachusetts Amherst, Amherst MA

Investigators

Abstract

Recent progress in bioinformatics, natural language understanding, computer vision, information retrieval and other areas has been significantly enabled by "conditional random fields" (CRFs)---machine learning models of structured outputs, such as sequences, trees and grids. However, many of the fundamental problems in these application areas involve not just fixed structures, but structures that must be inferred. This structural ambiguity arises from interacting choices at different levels of representation (e.g. from character sequences to meaning, or from pixels to scene interpretation). The project will move conditional random fields (CRFs) beyond fixed graphical structures to structures that are constructed dynamically during inference. Such a capability will be key to building next-generation systems that solve, not just an individual piece of a problem, but complex multi-step problems, as found in natural language understanding and computer vision, in a unified way.