A Unified Representation and Inference Paradigm for Natural Language
Dan Roth, Mark Sammons · Illinois Digital Environment for Access to Learning and Scholarship (University of Illinois at Urbana-Champaign) · 2008
Traditional approaches to Natural Language Text Processing limit performance and flexibility by committing to canonical reprentations of input text, while many NLP applications for general tasks such as Textual Entailment use ad-hoc architectures with limited flexibility, and which limit the expressiveness of inference procedures over components. We present a Modular Representation and Comparison Scheme (MRCS) that addresses these problems by combining a modular representation with a modular, unification-like inference algorithm that allows the system architect to defer appropriate disambiguation decisions until run-time.