Mixing Context Granularities for Improved Entity Linking on Question Answering Data across Entity Categories
This work addresses entity linking in question answering, which is crucial for improving knowledge base systems, though it appears incremental as it builds on existing methods with specific enhancements.
The paper tackles entity linking for knowledge base question answering by proposing a neural architecture that jointly optimizes mention detection and disambiguation using multi-granular context modeling. It achieves an average 8% improvement over the previous state-of-the-art on question answering benchmarks.
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.