Faster (and better) entity linking with cascades

Entity linking requires ranking thousands of candidates for each query, a time consuming process and a challenge for large scale linking. Many systems rely on prediction cascades to efficiently rank candidates. However, the design of these cascades often requires manual decisions about pruning and feature use, limiting the effectiveness of cascades. We present Slinky, a modular, flexible, fast and accurate entity linker based on prediction cascades. We adapt the web-ranking prediction cascade learning algorithm, Cronus, in order to learn cascades that are both accurate and fast. We show that by balancing between accurate and fast linking, this algorithm can produce Slinky configurations that are significantly faster and more accurate than a baseline configuration and an alternate cascade learning method with a fixed introduction of features.