# Improving Entity Linking by Modeling Latent Relations between Mentions

arXiv: https://arxiv.org/abs/1804.10637 · HTML: https://mlsanity.com/paper/1804.10637/

## Key facts

- Cited by 53 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 27 Apr 2018.

## Built on by (most cited first, 30 of 53)

- [Grammar-Constrained Decoding for Structured NLP Tasks without Finetuning](https://mlsanity.com/paper/2305.13971/) (2023)
- [CM3: A Causal Masked Multimodal Model of the Internet](https://mlsanity.com/paper/2201.07520/) (2022)
- [Unified Named Entity Recognition as Word-Word Relation Classification](https://mlsanity.com/paper/2112.10070/) (2021)
- [ReFinED: An Efficient Zero-shot-capable Approach to End-to-End Entity Linking](https://mlsanity.com/paper/2207.04108/) (2022)
- [Locate and Label: A Two-stage Identifier for Nested Named Entity Recognition](https://mlsanity.com/paper/2105.06804/) (2021)
- [GenIE: Generative Information Extraction](https://mlsanity.com/paper/2112.08340/) (2021)
- [WikiDiverse: A Multimodal Entity Linking Dataset with Diversified Contextual Topics and Entity Types](https://mlsanity.com/paper/2204.06347/) (2022)
- [Knowledge-Rich Self-Supervision for Biomedical Entity Linking](https://mlsanity.com/paper/2112.07887/) (2021)
- [Entity Linking and Discovery via Arborescence-based Supervised Clustering](https://mlsanity.com/paper/2109.01242/) (2021)
- [Entity Linking Meets Deep Learning: Techniques and Solutions](https://mlsanity.com/paper/2109.12520/) (2021)
- [Highly Parallel Autoregressive Entity Linking with Discriminative Correction](https://mlsanity.com/paper/2109.03792/) (2021)
- [Parallel Instance Query Network for Named Entity Recognition](https://mlsanity.com/paper/2203.10545/) (2022)
- [KILM: Knowledge Injection into Encoder-Decoder Language Models](https://mlsanity.com/paper/2302.09170/) (2023)
- [LLMAEL: Large Language Models are Good Context Augmenters for Entity Linking](https://mlsanity.com/paper/2407.04020/) (2024)
- [Multi-Grained Multimodal Interaction Network for Entity Linking](https://mlsanity.com/paper/2307.09721/) (2023)
- [Embracing Ambiguity: Improving Similarity-oriented Tasks with Contextual Synonym Knowledge](https://mlsanity.com/paper/2211.10997/) (2022)
- [MOLEMAN: Mention-Only Linking of Entities with a Mention Annotation Network](https://mlsanity.com/paper/2106.07352/) (2021)
- [MuVER: Improving First-Stage Entity Retrieval with Multi-View Entity Representations](https://mlsanity.com/paper/2109.05716/) (2021)
- [OneNet: A Fine-Tuning Free Framework for Few-Shot Entity Linking via Large Language Model Prompting](https://mlsanity.com/paper/2410.07549/) (2024)
- [Improving Entity Linking through Semantic Reinforced Entity Embeddings](https://mlsanity.com/paper/2106.08495/) (2021)
- [Kuaipedia: a Large-scale Multi-modal Short-video Encyclopedia](https://mlsanity.com/paper/2211.00732/) (2022)
- [ChatEL: Entity Linking with Chatbots](https://mlsanity.com/paper/2402.14858/) (2024)
- [Reddit Entity Linking Dataset](https://mlsanity.com/paper/2101.01228/) (2021)
- [Improving Entity Disambiguation by Reasoning over a Knowledge Base](https://mlsanity.com/paper/2207.04106/) (2022)
- [Towards Consistent Document-level Entity Linking: Joint Models for Entity Linking and Coreference Resolution](https://mlsanity.com/paper/2108.13530/) (2021)
- [Survey on English Entity Linking on Wikidata](https://mlsanity.com/paper/2112.01989/) (2021)
- [Data-Efficient Autoregressive Document Retrieval for Fact Verification](https://mlsanity.com/paper/2211.09388/) (2022)
- [Community Question Answering Entity Linking via Leveraging Auxiliary Data](https://mlsanity.com/paper/2205.11917/) (2022)
- [Benchmarking Diverse-Modal Entity Linking with Generative Models](https://mlsanity.com/paper/2305.17337/) (2023)
- [BanglaAutoKG: Automatic Bangla Knowledge Graph Construction with Semantic Neural Graph Filtering](https://mlsanity.com/paper/2404.03528/) (2024)

## Abstract (arXiv)

Entity linking involves aligning textual mentions of named entities to their corresponding entries in a knowledge base. Entity linking systems often exploit relations between textual mentions in a document (e.g., coreference) to decide if the linking decisions are compatible. Unlike previous approaches, which relied on supervised systems or heuristics to predict these relations, we treat relations as latent variables in our neural entity-linking model. We induce the relations without any supervision while optimizing the entity-linking system in an end-to-end fashion. Our multi-relational model achieves the best reported scores on the standard benchmark (AIDA-CoNLL) and substantially outperforms its relation-agnostic version. Its training also converges much faster, suggesting that the injected structural bias helps to explain regularities in the training data.