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

翻译：每个知识基础问题解答方法的第一阶段是将输入问题中的实体连接起来。我们调查在回答问题的任务中连接的实体,并联合提出一个优化的神经结构,供实体提及检测和实体脱钩,以不同程度的颗粒为周围环境建模。我们使用维基数据知识库和现有问题解答数据集,为在回答问题的数据上连接的实体建立基准。我们的方法优于以前最先进的数据系统,导致最终得分平均提高8%。我们进一步表明,我们的模型在不同实体类别中表现良好。