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A Unified Representation and Inference Paradigm for Natural Language

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.