# Entity Linking and Discovery via Arborescence-based Supervised Clustering

arXiv: https://arxiv.org/abs/2109.01242 · HTML: https://mlsanity.com/paper/2109.01242/

## Key facts

- Cited by 22 papers in the map (data through 14 Oct 2026).
- Builds on 30 of its 66 references that are in the map.
- First posted on arXiv 2 Sep 2021.

## Builds on (most cited first, 30 of 30)

- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [PyTorch: An Imperative Style, High-Performance Deep Learning Library](https://mlsanity.com/paper/1912.01703/) (2019)
- [Distilling the Knowledge in a Neural Network](https://mlsanity.com/paper/1503.02531/) (2015)
- [Billion-scale similarity search with GPUs](https://mlsanity.com/paper/1702.08734/) (2017)
- [Autoregressive Entity Retrieval](https://mlsanity.com/paper/2010.00904/) (2020)
- [Learning Continuous Hierarchies in the Lorentz Model of Hyperbolic Geometry](https://mlsanity.com/paper/1806.03417/) (2018)
- [Self-Alignment Pretraining for Biomedical Entity Representations](https://mlsanity.com/paper/2010.11784/) (2020)
- [Distilling Knowledge from Reader to Retriever for Question Answering](https://mlsanity.com/paper/2012.04584/) (2020)
- [Poly-encoders: Transformer Architectures and Pre-training Strategies for Fast and Accurate Multi-sentence Scoring](https://mlsanity.com/paper/1905.01969/) (2019)
- [Zero-Shot Entity Linking by Reading Entity Descriptions](https://mlsanity.com/paper/1906.07348/) (2019)
- [Learning Dense Representations for Entity Retrieval](https://mlsanity.com/paper/1909.10506/) (2019)
- [End-to-End Neural Entity Linking](https://mlsanity.com/paper/1808.07699/) (2018)
- [Scalable Zero-shot Entity Linking with Dense Entity Retrieval](https://mlsanity.com/paper/1911.03814/) (2019)
- [MedMentions: A Large Biomedical Corpus Annotated with UMLS Concepts](https://mlsanity.com/paper/1902.09476/) (2019)
- [Deep Joint Entity Disambiguation with Local Neural Attention](https://mlsanity.com/paper/1704.04920/) (2017)
- [Biomedical Entity Representations with Synonym Marginalization](https://mlsanity.com/paper/2005.00239/) (2020)
- [Improving Entity Linking by Modeling Latent Relations between Mentions](https://mlsanity.com/paper/1804.10637/) (2018)
- [Revisiting Joint Modeling of Cross-document Entity and Event Coreference Resolution](https://mlsanity.com/paper/1906.01753/) (2019)
- [DeepType: Multilingual Entity Linking by Neural Type System Evolution](https://mlsanity.com/paper/1802.01021/) (2018)
- [Understanding Hard Negatives in Noise Contrastive Estimation](https://mlsanity.com/paper/2104.06245/) (2021)
- [Multi-step Entity-centric Information Retrieval for Multi-Hop Question Answering](https://mlsanity.com/paper/1909.07598/) (2019)
- [Streamlining Cross-Document Coreference Resolution: Evaluation and Modeling](https://mlsanity.com/paper/2009.11032/) (2020)
- [Learning to Link](https://mlsanity.com/paper/1907.00533/) (2019)
- [SciCo: Hierarchical Cross-Document Coreference for Scientific Concepts](https://mlsanity.com/paper/2104.08809/) (2021)
- [Ultrametric Fitting by Gradient Descent](https://mlsanity.com/paper/1905.10566/) (2019)
- [MOLEMAN: Mention-Only Linking of Entities with a Mention Annotation Network](https://mlsanity.com/paper/2106.07352/) (2021)
- [CD2CR: Co-reference Resolution Across Documents and Domains](https://mlsanity.com/paper/2101.12637/) (2021)
- [Towards Consistent Document-level Entity Linking: Joint Models for Entity Linking and Coreference Resolution](https://mlsanity.com/paper/2108.13530/) (2021)
- [Supervised Hierarchical Clustering with Exponential Linkage](https://mlsanity.com/paper/1906.07859/) (2019)
- [On Efficient Low Distortion Ultrametric Embedding](https://mlsanity.com/paper/2008.06700/) (2020)

## Built on by (most cited first, 22 of 22)

- [Generative Biomedical Entity Linking via Knowledge Base-Guided Pre-training and Synonyms-Aware Fine-tuning](https://mlsanity.com/paper/2204.05164/) (2022)
- [Maverick: Efficient and Accurate Coreference Resolution Defying Recent Trends](https://mlsanity.com/paper/2407.21489/) (2024)
- [Bring Your Own KG: Self-Supervised Program Synthesis for Zero-Shot KGQA](https://mlsanity.com/paper/2311.07850/) (2023)
- [Modeling Fine-grained Information via Knowledge-aware Hierarchical Graph for Zero-shot Entity Retrieval](https://mlsanity.com/paper/2211.10991/) (2022)
- [HunFlair2 in a cross-corpus evaluation of biomedical named entity recognition and normalization tools](https://mlsanity.com/paper/2402.12372/) (2024)
- [BELB: a Biomedical Entity Linking Benchmark](https://mlsanity.com/paper/2308.11537/) (2023)
- [Learning from Negative Samples in Biomedical Generative Entity Linking](https://mlsanity.com/paper/2408.16493/) (2024)
- [NASTyLinker: NIL-Aware Scalable Transformer-based Entity Linker](https://mlsanity.com/paper/2303.04426/) (2023)
- [Improving Dual-Encoder Training through Dynamic Indexes for Negative Mining](https://mlsanity.com/paper/2303.15311/) (2023)
- [CoRTEx: Contrastive Learning for Representing Terms via Explanations with Applications on Constructing Biomedical Knowledge Graphs](https://mlsanity.com/paper/2312.08036/) (2023)
- [Fast, Scalable, Warm-Start Semidefinite Programming with Spectral Bundling and Sketching](https://mlsanity.com/paper/2312.11801/) (2023)
- [BELHD: Improving Biomedical Entity Linking with Homonoym Disambiguation](https://mlsanity.com/paper/2401.05125/) (2024)
- [DynamicER: Resolving Emerging Mentions to Dynamic Entities for RAG](https://mlsanity.com/paper/2410.11494/) (2024)
- [Towards Better Entity Linking with Multi-View Enhanced Distillation](https://mlsanity.com/paper/2305.17371/) (2023)
- [xMEN: A Modular Toolkit for Cross-Lingual Medical Entity Normalization](https://mlsanity.com/paper/2310.11275/) (2023)
- [Span-Oriented Information Extraction -- A Unifying Perspective on Information Extraction](https://mlsanity.com/paper/2403.15453/) (2024)
- [Comparing Neighbors Together Makes it Easy: Jointly Comparing Multiple Candidates for Efficient and Effective Retrieval](https://mlsanity.com/paper/2405.12801/) (2024)
- [Explainable ICD Coding via Entity Linking](https://mlsanity.com/paper/2503.20508/) (2025)
- [SynCABEL: Synthetic Contextualized Augmentation for Biomedical Entity Linking](https://mlsanity.com/paper/2601.19667/) (2026)
- [Mah=an=ama: A Unique Testbed for Literary Entity Discovery and Linking](https://mlsanity.com/paper/2509.19844/) (2025)
- [LongBEL: Long-Context and Document-Consistent Biomedical Entity Linking](https://mlsanity.com/paper/2605.13451/) (2026)
- [BELXTR: Biomedical Entity Linking via Contextualized Token Retrieval](https://mlsanity.com/paper/2609.25859/) (2026)

## Abstract (arXiv)

Previous work has shown promising results in performing entity linking by measuring not only the affinities between mentions and entities but also those amongst mentions. In this paper, we present novel training and inference procedures that fully utilize mention-to-mention affinities by building minimum arborescences (i.e., directed spanning trees) over mentions and entities across documents in order to make linking decisions. We also show that this method gracefully extends to entity discovery, enabling the clustering of mentions that do not have an associated entity in the knowledge base. We evaluate our approach on the Zero-Shot Entity Linking dataset and MedMentions, the largest publicly available biomedical dataset, and show significant improvements in performance for both entity linking and discovery compared to identically parameterized models. We further show significant efficiency improvements with only a small loss in accuracy over previous work, which use more computationally expensive models.