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

arXiv: https://arxiv.org/abs/1804.08460 · HTML: https://mlsanity.com/paper/1804.08460/

## Key facts

- Cited by 9 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 23 Apr 2018.

## Built on by (most cited first, 9 of 9)

- [Awakening Latent Grounding from Pretrained Language Models for Semantic Parsing](https://mlsanity.com/paper/2109.10540/) (2021)
- [End-to-End Entity Resolution and Question Answering Using Differentiable Knowledge Graphs](https://mlsanity.com/paper/2109.05817/) (2021)
- [Survey on English Entity Linking on Wikidata](https://mlsanity.com/paper/2112.01989/) (2021)
- [Know What I don't Know: Handling Ambiguous and Unanswerable Questions for Text-to-SQL](https://mlsanity.com/paper/2212.08902/) (2022)
- [Namesakes: Ambiguously Named Entities from Wikipedia and News](https://mlsanity.com/paper/2111.11372/) (2021)
- [Named Entity Linking with Entity Representation by Multiple Embeddings](https://mlsanity.com/paper/2205.10498/) (2022)
- [Query Brand Entity Linking in E-Commerce Search](https://mlsanity.com/paper/2502.01555/) (2025)
- [Entity-Augmented Neuroscience Knowledge Retrieval Using Ontology and Semantic Understanding Capability of LLM](https://mlsanity.com/paper/2506.03145/) (2025)
- [Dynamic Injection of Entity Knowledge into Dense Retrievers](https://mlsanity.com/paper/2507.03922/) (2025)

## Abstract (arXiv)

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.