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The first stage of every knowledge base question answering approach is tolink entities in the input question. We investigate entity linking in thecontext of a question answering task and present a jointly optimized neuralarchitecture for entity mention detection and entity disambiguation that modelsthe surrounding context on different levels of granularity. We use the Wikidataknowledge base and available question answering datasets to create benchmarksfor entity linking on question answering data. Our approach outperforms theprevious state-of-the-art system on this data, resulting in an average 8%improvement of the final score. We further demonstrate that our model deliversa strong performance across different entity categories.

Mixing Context Granularities for Improved Entity Linking on Question Answering Data across Entity Categories