EL

Noise Robust Named Entity Understanding for Voice Assistants (2021.naacl-industry)

Challenge:	a biomedical entity linking system is available for COVID-19 research.
Approach:	They propose a biomedical entity linking system that detects named enti- ties in text and links them to the UMLS knowledge base.
Outcome:	The proposed system detects named enti- ties in text and links them to the unified medical language system (UMS) knowledge base entries.

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Deepak Muralidharan, Joel Ruben Antony Moniz, Sida Gao, Xiao Yang, Justine Kao, Stephen Pulman, Atish Kothari, Ray Shen, Yinying Pan, Vivek Kaul, Mubarak Seyed Ibrahim, Gang Xiang, Nan Dun, Yidan Zhou, Andy O, Yuan Zhang, Pooja Chitkara, Xuan Wang, Alkesh Patel, Kushal Tayal, Roger Zheng, Peter Grasch, Jason D Williams, Lin Li

Robustness in NLP

Challenge:	Named Entity Recognition and Entity Linking are challenging for voice assistants . utterances are relatively short, so there is not much context to help disambiguate .
Approach:	They propose a Named Entity Understanding system that combines NER and EL in a joint reranking module.
Outcome:	The proposed framework improves NER accuracy by up to 3.13% and EL accuracy by 3.6% in F1 score . it also leads to better accuracies in other natural language understanding tasks .

Joint Learning of Named Entity Recognition and Entity Linking (P19-2)

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Pedro Henrique Martins, Zita Marinho, André F. T. Martins

Learning Dynamic Context Augmentation for Global Entity Linking (D19-1)

Challenge:	Named entity recognition and entity linking are two fundamentally related tasks . most approaches focus on the mention detection part, assuming the correct mentions have been detected .
Approach:	They perform joint learning of named entity recognition and entity linking to leverage their relatedness.
Outcome:	The proposed model achieves competitive results with the state-of-the-art in both NER and EL tasks.

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Xiyuan Yang, Xiaotao Gu, Sheng Lin, Siliang Tang, Yueting Zhuang, Fei Wu, Zhigang Chen, Guoping Hu, Xiang Ren

Entity Linking in the Job Market Domain (2024.findings-eacl)

Challenge:	Existing collective entity linking methods are expensive and often lack local context information.
Approach:	They propose a dynamic context-augmented inference model that can be used to make collective inference.
Outcome:	The proposed model can cope with different local EL models with different learning settings, base models, decision orders and attention mechanisms.

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Mike Zhang, Rob Goot, Barbara Plank

CHOLAN: A Modular Approach for Neural Entity Linking on Wikipedia and Wikidata (2021.eacl-main)

Challenge:	In Natural Language Processing, entity linking (EL) has centered around Wikipedia, but yet remains underexplored for the job market domain.
Approach:	They propose to use a bi-encoder and an autoregressive model to link fine-grained span-level skill mentions to a specific taxonomy entry to quantify labor market demands.
Outcome:	The proposed model outperforms GENRE in strict evaluation, but performs better in loose evaluation.

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Manoj Prabhakar Kannan Ravi, Kuldeep Singh, Isaiah Onando Mulang’, Saeedeh Shekarpour, Johannes Hoffart, Jens Lehmann

entity-linkings: A Unified Library for Entity Linking (2026.eacl-demo)

Challenge:	Existing approaches to target end-to-end entity linking over knowledge bases are not efficient.
Approach:	They propose a modular approach to target end-to-end entity linking over knowledge bases.
Outcome:	The proposed approach outperforms state-of-the-art approaches on two well-known knowledge bases.

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Yuya Sawada, Tsuyoshi Fujita, Yusuke Sakai, Taro Watanabe

Disease Network Constructor: a Pathway Extraction and Visualization (2023.acl-demo)

Challenge:	Entity linking (EL) is the task of mapping named entities in text to canonical entries in a knowledge base.
Approach:	They propose a unified library for using and developing entity linking systems . a strong emphasis is placed on usability, making it highly extensible .
Outcome:	a new library aims to disambiguate named entities in text by mapping them to canonical entries in a knowledge base.

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Mohammad Golam Sohrab, Khoa Duong, Goran Topić, Masami Ikeda, Nozomi Nagano, Yayoi Natsume-Kitatani, Masakata Kuroda, Mari Itoh, Hiroya Takamura

Leveraging the Power of Large Language Models in Entity Linking via Adaptive Routing and Targeted Reasoning (2025.emnlp-industry)

Challenge:	In the human body, various substances (entities) such as proteins and compounds interact and regulate each other, forming huge pathway networks.
Approach:	They present a system that extracts and visualizes a disease network derived through regulation events found in scientific articles on idiopathic pulmonary fibrosis.
Outcome:	The proposed system extracts and visualizes a disease network from biomedical articles on idiopathic pulmonary fibrosis (IPF) it includes two-dimensional (2D) and 3D visualizations of the constructed disease network.

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Yajie Li, Albert Galimov, Mitra Datta Ganapaneni, Pujitha Thejaswi, De Meng, Priyanshu Kumar, Saloni Potdar

A Comprehensive Evaluation of Biomedical Entity-centric Search (2022.emnlp-industry)

Challenge:	Entity Linking (EL) relies on large labeled datasets and extensive fine-tuning . lexical ambiguity, knowledge-intensive cases and low-context mentions are some of the challenges.
Approach:	Entity Linking (EL) relies on large annotated datasets and extensive fine-tuning . authors propose a pipeline that integrates candidate generation, context-based scoring, adaptive routing, and selective reasoning .
Outcome:	ARTER outperforms ReFinED and LLM-based pipelines on standard benchmarks . it performs twice as efficiently on 5 out of 6 datasets and doubles the number of tokens compared to pipelines using LLM .

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Elena Tutubalina, Zulfat Miftahutdinov, Vladimir Muravlev, Anastasia Shneyderman

Information Retrieval

Challenge:	a novel algorithm for information retrieval from biomedical abstracts is used to identify entities.
Approach:	They perform a fine-grained evaluation of a BERT-based biomedical search engine . they use manually annotated PubMed abstracts and off-she-shelf Elasticsearch .
Outcome:	The proposed system performs better for disease and gene search queries than other systems.

Contextualized End-to-End Neural Entity Linking (2020.aacl-main)

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Haotian Chen, Xi Li, Andrej Zukov Gregoric, Sahil Wadhwa

LNN-EL: A Neuro-Symbolic Approach to Short-text Entity Linking (2021.acl-long)

Challenge:	a proposed entity linking model that disjointly applies MD and ED from the same contextualized BERT embeddings is able to generalize better.
Approach:	They propose an entity linking (EL) model that jointly learns mention detection (MD) and entity disambiguation (ED) they propose to use task-specific heads on top of shared BERT contextualized embeddings to learn MD and ED.
Outcome:	The proposed model achieves state-of-the-art results across a standard EL dataset and under a setting where hand-crafted candidate sets are not available.

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Hang Jiang, Sairam Gurajada, Qiuhao Lu, Sumit Neelam, Lucian Popa, Prithviraj Sen, Yunyao Li, Alexander Gray

Fine-Grained Evaluation for Entity Linking (D19-1)

Challenge:	Existing work deals with EL in the context of longer text, such as a sentence.
Approach:	They propose a neuro-symbolic approach that uses interpretable rules based on first-order logic to achieve better performance with black-box neural approaches.
Outcome:	The proposed approach achieves better performance than heuristics-based approaches on short-text EL . it can easily blend existing rule templates with multiple types of features, and even with scores resulting from previous EL methods.

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Henry Rosales-Méndez, Aidan Hogan, Barbara Poblete

Lightweight Domain-Specific Language Model for Real-Time Structuring of Medical Prescriptions (2026.eacl-industry)

Challenge:	Entity Linking (EL) is an Information Extraction task that identifies entity mentions in a text corpus and associates them with an unambiguous identifier in KBs such as Wikipedia, BabelNet, DBpedia, Wikidata and YAGO.
Approach:	They propose a fine-grained categorization of different types of entity mentions and links and propose 'fuzzy recall' metric to address the lack of consensus and compare a selection of online EL systems.
Outcome:	The proposed task offers a bridge between unstructured text and structured KBs, where EL has applications for semantic search, document classification, relation extraction, and more.

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Jonathan Pattin Cottet, Véronique Eglin, Alex Aussem

Language Models

Challenge:	Existing language models ignore layout information, rely on expensive image-based architectures, or cannot operate under privacy and hardware constraints.
Approach:	They propose a lightweight, privacy-preserving transformer specifically designed for Entity Extraction (EE) and Entity Linking (EL) in french medical prescriptions.
Outcome:	The proposed model matches or surpasses larger document-understanding models on strict extraction metrics while maintaining essential spatial cues.

E-BERT: Efficient-Yet-Effective Entity Embeddings for BERT (2020.findings-emnlp)

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Nina Poerner, Ulli Waltinger, Hinrich Schütze

A Multilingual Wikified Data Set of Educational Material (L18-1)

Challenge:	Existing methods to enhance BERT with factual knowledge about entities require no additional pretraining and no changes to the encoder itself.
Approach:	They propose a way to inject factual knowledge into the pretrained BERT model by aligning Wikipedia2Vec entity vectors with BERT's native wordpiece vector space and feeding the aligned entity vector into BERT as if they were wordpieces.
Outcome:	The proposed version outperforms baseline models on unsupervised question answering, supervised relation classification and entity linking tasks.

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Iris Hendrickx, Eirini Takoulidou, Thanasis Naskos, Katia Lida Kermanidis, Vilelmini Sosoni, Hugo de Vos, Maria Stasimioti, Menno van Zaanen, Panayota Georgakopoulou, Valia Kordoni, Maja Popovic, Markus Egg, Antal van den Bosch

Multilinguality

Challenge:	a crowdsourcing effort to annotate and link parallel texts has been unsuccessful . a data set of parallel texts in eleven languages is presented .
Approach:	They present a wikified data set of English sentences linked to Wikipedia pages . they use crowdsourcing to annotate the texts and perform crowdsourcing for complex annotations .
Outcome:	The proposed data set is valuable as it constitutes a rich resource . it includes annotated data of English sentences linked to translations in eleven languages .

Towards Consistent Document-level Entity Linking: Joint Models for Entity Linking and Coreference Resolution (2022.acl-short)

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Klim Zaporojets, Johannes Deleu, Yiwei Jiang, Thomas Demeester, Chris Develder

Zero-shot Entity Linking with Less Data (2022.findings-naacl)

Challenge:	Existing approaches to solve entity linking (EL) jointly with coreference resolution (coref) a coreferenced cluster can only be linked to a single entity or NIL (i.e., a nonlinkable entity)
Approach:	They propose to join entity linking and coreference resolution in a single structured prediction task over directed trees and use a globally normalized model to solve it.
Outcome:	The proposed model improves on two datasets with a +5% boost in accuracy compared to standalone models . the proposed model is based on current models that predict a single antecedent for each span to resolve .

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G P Shrivatsa Bhargav, Dinesh Khandelwal, Saswati Dana, Dinesh Garg, Pavan Kapanipathi, Salim Roukos, Alexander Gray, L Venkata Subramaniam

Instructed Language Models with Retrievers Are Powerful Entity Linkers (2023.emnlp-main)

Challenge:	Entity linking maps an entity mention in a natural language sentence to an entity in KB.
Approach:	They propose a neuro-symbolic, multi-task learning approach to bridge this gap by exploiting an auxiliary information about entity types.
Outcome:	The proposed approach achieves significantly higher performance on four different benchmark datasets when trained with just 0.01%, 0.1%, or 1% of the training data.

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Zilin Xiao, Ming Gong, Jie Wu, Xingyao Zhang, Linjun Shou, Daxin Jiang

ELDEN: Improved Entity Linking Using Densified Knowledge Graphs (N18-1)

Challenge:	Generative approaches powered by large language models have demonstrated emergent abilities in tasks that require complex reasoning abilities.
Approach:	They propose a sequence-to-sequence training objective with instruction-tuning that enables casual language models to perform entity linking over knowledge bases.
Outcome:	The proposed framework outperforms existing approaches with +6.8 F1 points gain on average and huge advantage in training data efficiency and compute consumption.

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Priya Radhakrishnan, Partha Talukdar, Vasudeva Varma

Injecting Knowledge Base Information into End-to-End Joint Entity and Relation Extraction and Coreference Resolution (2021.findings-acl)

Challenge:	Entity Linking (EL) systems aim to automatically map mentions of an entity in text to the corresponding entity in Knowledge Graph (KG).
Approach:	They propose to densify the Knowledge Graph (KG) with co-occurrence statistics and then use the densified KG to train entity embeddings.
Outcome:	The proposed system outperforms state-of-the-art EL systems on benchmark datasets and outperformed state- of-the art systems on sparsely connected entities in the KG.

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Severine Verlinden, Klim Zaporojets, Johannes Deleu, Thomas Demeester, Chris Develder

It’s All About the Confidence: An Unsupervised Approach for Multilingual Historical Entity Linking using Large Language Models (2026.eacl-long)

Challenge:	Using unsupervised entity linking, we solve named entity recognition, coreference resolution and relation extraction tasks together.
Approach:	They propose to use a knowledge base to inject information into a joint IE model by using unsupervised entity linking.
Outcome:	The proposed model improves on two datasets with 5% F1 score.

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Cristian Santini, Marieke van Erp, Mehwish Alam

S2abEL: A Dataset for Entity Linking from Scientific Tables (2023.emnlp-main)

Challenge:	Existing approaches to EL for historical texts require substantial training data or rely on domain-specific rules that limit scalability.
Approach:	They propose an unsupervised ensemble approach combining a Small Language Model and an LLM for historical EL.
Outcome:	The proposed approach outperforms state-of-the-art models on four established benchmarks in six European languages from the 19th and 20th centuries.

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Yuze Lou, Bailey Kuehl, Erin Bransom, Sergey Feldman, Aakanksha Naik, Doug Downey

Multimodality

Challenge:	Entity linking (EL) is a longstanding problem in natural language processing and information extraction.
Approach:	They propose a neural baseline method for EL on scientific tables containing many out-of-knowledge-base mentions and a method that significantly outperforms a generic table EL method.
Outcome:	The proposed method significantly outperforms state-of-the-art generic table EL method on scientific tables with many out-of knowledge-base mentions.

Named Entity Recognition for Entity Linking: What Works and What’s Next (2021.findings-emnlp)

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Simone Tedeschi, Simone Conia, Francesco Cecconi, Roberto Navigli

AELC: Adaptive Entity Linking with LLM-Driven Contextualization (2025.findings-emnlp)

Challenge:	Entity Linking (EL) systems have achieved impressive results on standard benchmarks thanks to the contextualized representations provided by recent pretrained language models.
Approach:	They propose to exploit Named Entity Recognition (NER) to narrow the gap between EL systems trained on high and low amounts of labeled data.
Outcome:	The proposed model can be exploited to narrow the gap between EL systems trained on high and low amounts of labeled data.

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Fang Wang, Zhengwei Tao, Ming Wang, Minghao Hu, Xiaoying Bai

COMETA: A Corpus for Medical Entity Linking in the Social Media (2020.emnlp-main)

Challenge:	Entity linking (EL) focuses on associating ambiguous mentions in text with corresponding entities in a knowledge graph.
Approach:	Entity linking (EL) focuses on associating ambiguous mentions in text with corresponding entities in a knowledge graph.
Outcome:	Experiments on four public benchmark datasets show that AELC achieves state-of-the-art performance.

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Marco Basaldella, Fangyu Liu, Ehsan Shareghi, Nigel Collier

ChatEL: Entity Linking with Chatbots (2024.lrec-main)

Challenge:	Existing datasets for Entity Linking (EL) fail to address the complex nature of health terminology in layman’s language.
Approach:	They propose to use a corpus of 20k English biomedical entity mentions from Reddit expert-annotated with links to a widely-used medical knowledge graph to investigate the ability of these systems to perform complex inference on entities and concepts.
Outcome:	The proposed corpus satisfies a combination of desirable properties, from scale and coverage to diversity and quality, that to the best of our knowledge has not been met by existing resources in the field.

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Yifan Ding, Qingkai Zeng, Tim Weninger

Improving Candidate Retrieval with Entity Profile Generation for Wikidata Entity Linking (2022.findings-acl)

Challenge:	Entity Linking (EL) is a challenging task in natural language processing . existing approaches focus on creating elaborate contextual models that are unwieldy and difficult to train .
Approach:	They propose a framework to prompt LLMs to return accurate results for Entity Linking . they use a three-step framework to generate a set of EL models that can be open-source .
Outcome:	The proposed framework improves the average F1 performance across 10 datasets by more than 2%.

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Tuan Lai, Heng Ji, ChengXiang Zhai

Generative Biomedical Entity Linking via Knowledge Base-Guided Pre-training and Synonyms-Aware Fine-tuning (2022.naacl-main)

Challenge:	Existing studies focus on Wikipedia-derived KBs, but there is little work on EL over Wikidata . EL systems have found applications in many tasks such as question answering .
Approach:	They propose a novel approach to linking entity mentions to referent entities in a knowledge base . they use a sequence-to-sequence model to generate the profile of the target entity .
Outcome:	The proposed approach achieves state-of-the-art results on three Wikidata-based datasets and strong performance on TACKBP-2010.

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Hongyi Yuan, Zheng Yuan, Sheng Yu

Entity Disambiguation via Fusion Entity Decoding (2024.naacl-long)

Challenge:	Generative methods for biomedical entity linking (EL) use synonyms knowledge from knowledge bases (KB) this is not trivial to inject into a generative method, but it is cost-effective.
Approach:	They propose to inject synonyms knowledge into a generative model of biomedical EL by constructing synthetic samples with synonyms and definitions from KB and requiring the model to recover concept names.
Outcome:	The proposed method achieves state-of-the-art results on several biomedical EL tasks without candidate selection.

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Junxiong Wang, Ali Mousavi, Omar Attia, Ronak Pradeep, Saloni Potdar, Alexander Rush, Umar Farooq Minhas, Yunyao Li

Enhanced Entity Annotations for Multilingual Corpora (2022.lrec-1)

Challenge:	Existing generative approaches demonstrate improved accuracy compared to classification approaches under the standardized ZELDA benchmark.
Approach:	They propose an encoder-decoder model to disambiguate entities with more detailed entity descriptions.
Outcome:	The proposed model outperforms existing classification models on the ZELDA benchmark and on retrieval/reader frameworks.

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Michael Strobl, Amine Trabelsi, Osmar Zaïane

Benchmarking Diverse-Modal Entity Linking with Generative Models (2023.findings-acl)

Challenge:	Named Entity Recognition (NER) is a new language for natural language processing.
Approach:	They propose to improve the annotation quality of the English Wikipedia tool WEXEA . they propose to use a proven NER system to annotate entities in Wikipedia .
Outcome:	The proposed tool can be used to exhaustively annotate entities in Wikipedia articles.

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Sijia Wang, Alexander Hanbo Li, Henghui Zhu, Sheng Zhang, Pramuditha Perera, Chung-Wei Hang, Jie Ma, William Yang Wang, Zhiguo Wang, Vittorio Castelli, Bing Xiang, Patrick Ng

Multimodality

Challenge:	Existing models for diverse-mode entity linking (EL) work well on per modality configurations, but it is more challenging to design a unified model for diverse modality.
Approach:	They propose a generative diverse-modal model that integrates text, image and table . they propose combining a multimodal encoder-decoder paradigm with a fine-tuning GDMM .
Outcome:	The proposed model outperforms state-of-the-art models by 8.51 F1 on average for diverse-modal EL.

Evidence-guided Inference for Neutralized Zero-shot Transfer (2024.lrec-main)

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Xiaotong Feng, Meng-Fen Chiang, Wang-Chien Lee, Zixin Kuang

Responsible & Trustworthy NLP

Challenge:	Existing knowledge transfer frameworks that use label skewness to neutralize biased language are costly and impractical when it comes to scarcely labeled data.
Approach:	They propose a neutralized Knowledge Transfer framework to equip pre-trained language models with neutralized transferability.
Outcome:	The proposed framework shows that it can be used to train pre-trained models with neutralized transferability . it is compared with baselines with a zero-shot cross-domain transfer setting .

Highly Parallel Autoregressive Entity Linking with Discriminative Correction (2021.emnlp-main)

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Nicola De Cao, Wilker Aziz, Ivan Titov

From Zero to Hero: Human-In-The-Loop Entity Linking in Low Resource Domains (2020.acl-main)

Challenge:	Existing approaches to EL have been shown to be effective for both Entity Disambiguation and Entity Linking, but they suffer from high computational cost due to a complex (deep) decoder and the need for training on a large amount of data.
Approach:	They propose a method that parallelizes autoregressive linking across all potential mentions and relies on a shallow and efficient decoder.
Outcome:	The proposed method outperforms state-of-the-art approaches on the English dataset AIDA-CoNLL and is >70 times faster and more accurate than the previous generative method.

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Jan-Christoph Klie, Richard Eckart de Castilho, Iryna Gurevych

OneNet: A Fine-Tuning Free Framework for Few-Shot Entity Linking via Large Language Model Prompting (2024.emnlp-main)

Challenge:	Existing approaches to disambiguate entity mentions in a text depend on training data.
Approach:	They propose a domain-agnostic approach to annotate entities using a KB-based approach.
Outcome:	The proposed approach outperforms existing methods in a simulation on difficult texts.

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Xukai Liu, Ye Liu, Kai Zhang, Kehang Wang, Qi Liu, Enhong Chen

Robustness Evaluation of Entity Disambiguation Using Prior Probes: the Case of Entity Overshadowing (2021.emnlp-main)

Challenge:	Entity Linking (EL) is the process of associating ambiguous textual mentions to specific entities in a knowledge base.
Approach:	They propose a framework that utilizes the few-shot learning capabilities of Large Language Models without the need for fine-tuning to improve the accuracy of EL.
Outcome:	The framework outperforms current state-of-the-art methods in a few-shot entity linking task.

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Vera Provatorova, Samarth Bhargav, Svitlana Vakulenko, Evangelos Kanoulas

Robustness in NLP

Challenge:	Entity disambiguation (ED) is the last step of entity linking when candidate entities are reranked according to the context they appear in.
Approach:	They propose a dataset that includes 16K short text snippets annotated with entity mentions to evaluate EL models.
Outcome:	The proposed dataset shows that the performance of EL systems is overestimated . the results show that the EL system performance is significantly better on the ShadowLink benchmark .

Real World Conversational Entity Linking Requires More Than Zero-Shots (2024.findings-acl)

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Mohanna Hoveyda, Arjen Vries, Faegheh Hasibi, Maarten Rijke

ReLiK: Retrieve and LinK, Fast and Accurate Entity Linking and Relation Extraction on an Academic Budget (2024.findings-acl)

Challenge:	Entity linking (EL) in conversations is a key component of many downstream tasks such as semantic search.
Approach:	They propose to use Fandom and Wikipedia to evaluate EL models' ability to generalize to a new unfamiliar KB without prior training.
Outcome:	The proposed evaluation framework and dataset are tailored to facilitate the study.

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Riccardo Orlando, Pere-Lluís Huguet Cabot, Edoardo Barba, Roberto Navigli

RoCEL: Advancing Table Entity Linking through Distinctive Row and Column Contexts (2024.emnlp-main)

Challenge:	Entity Linking and Relation Extraction (EL) are fundamental tasks in Natural Language Processing.
Approach:	They propose a Retriever-Reader architecture for Entity Linking and Relation Extraction . they propose an input representation that incorporates the candidate entities alongside the text .
Outcome:	The proposed architecture achieves state-of-the-art in in- and out-of domain benchmarks while using academic budget training and with 40x inference speed compared to competitors.

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Yuanzheng Wang, Yixing Fan, Jiafeng Guo, Ruqing Zhang, Xueqi Cheng

Multimodality

Challenge:	Existing methods for table entity linking ignore row and column contexts . existing methods for TEL focus on understanding sequential text contexts, making it difficult to adapt to the row and columns structure of tables.
Approach:	They propose to leverage row and column contexts to enhance the semantics of mentions in entity disambiguation.
Outcome:	The proposed method outperforms the state-of-the-art (SOTA) baseline by 1.5% on the in-domain dataset and 3.7% on average across three out-of domain datasets.

A Read-and-Select Framework for Zero-shot Entity Linking (2023.findings-emnlp)

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Zhenran Xu, Yulin Chen, Baotian Hu, Min Zhang