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A key challenge in entity linking is making effective use of contextual information to dis-ambiguate mentions that might refer to different entities in different contexts. We present a model that uses convolutional neural networks to capture semantic correspondence between a mention's context and a proposed target entity. These convolutional networks(More)
—Structured prediction algorithms—used when applying machine learning to tasks like natural language parsing and image understanding—present some opportunities for fine-grained parallelism, but also have problem-specific serial dependencies. Most implementations exploit only simple opportunities such as parallel BLAS, or embarrassing parallelism over input(More)
Declarative programming is a paradigm that allows programmers to specify what they want to compute, leaving how to compute it to a solver. Our declarative programming language, Dyna, is designed to compactly specify computations like those that are frequently encountered in machine learning. As a declarative language, Dyna’s solver has a large space(More)
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