Papers with Entity linking
LLM as Entity Disambiguator for Biomedical Entity-Linking (2025.acl-short)
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| Challenge: | Entity linking involves normalizing a mention in medical text to a unique identifier in a knowledge base, such as UMLS or MeSH. |
| Approach: | They propose to use a large language model as an entity disambiguator to enhance the accuracy of alias-matching entity linking methods. |
| Outcome: | The proposed method surpasses existing methods on biomedical datasets by up to 16 points in accuracy. |
entity-linkings: A Unified Library for Entity Linking (2026.eacl-demo)
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| 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. |
LNN-EL: A Neuro-Symbolic Approach to Short-text Entity Linking (2021.acl-long)
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Hang Jiang, Sairam Gurajada, Qiuhao Lu, Sumit Neelam, Lucian Popa, Prithviraj Sen, Yunyao Li, Alexander Gray
| 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. |
Selecting Key Views for Zero-Shot Entity Linking (2023.findings-emnlp)
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| Challenge: | Entity linking is a task of assigning ambiguous mentions in textual input to entities in knowledge bases. |
| Approach: | They propose a framework to align mentions in text to entities in knowledge bases . they use unsupervised clustering to select key views from descriptions . |
| Outcome: | The proposed framework achieves state-of-the-art on the zero-shot entity linking dataset. |
Improving Entity Linking by Modeling Latent Relations between Mentions (P18-1)
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| Challenge: | Entity linking systems often exploit relations between textual mentions to decide if the linking decisions are compatible. |
| Approach: | They treat relations as latent variables while optimizing the neural entity-linking model without supervision. |
| Outcome: | The proposed model outperforms its relation-agnostic version and significantly outperformed its relational version. |
S2abEL: A Dataset for Entity Linking from Scientific Tables (2023.emnlp-main)
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| 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. |
Improving Zero-Shot Entity Linking Candidate Generation with Ultra-Fine Entity Type Information (2022.coling-1)
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| Challenge: | Entity linking is a task of assigning entity mentions to referent entities in a knowledge base. |
| Approach: | They propose to use ultra-fine-grained type information to improve the generalization ability of EL models by utilizing a low-level task to extract ultra-finish entity type information. |
| Outcome: | The proposed model achieves state-of-the-art in the zero-shot entity linking task . |
AELC: Adaptive Entity Linking with LLM-Driven Contextualization (2025.findings-emnlp)
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| 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. |
OpenEL: An Annotated Corpus for Entity Linking and Discourse in Open Domain Dialogue (2022.lrec-1)
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| Challenge: | Named entity recognition (NER), named entity linking and discourse modeling are crucial aspects of natural language understanding for open domain dialogue systems. |
| Approach: | They present an annotated multi-domain corpus for linking entities in open-domain dialogue . they use dialogue context and anaphora resolution to assess the effectiveness of the task . |
| Outcome: | The OpenEL corpus is an annotated multi-domain corpus for linking entities in open-domain dialogue . the system Flair + BLINK has the best performance with a 0.65 F1 score . |
CLEEK: A Chinese Long-text Corpus for Entity Linking (2020.lrec-1)
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| Challenge: | Entity linking is a fundamental task in natural language processing, says nigel kilgstrom . existing corpora for entity linking in china are lacking and deficient, he says . kilsmstrom: a new method for entity disambiguation can be developed for Chinese . |
| Approach: | They build a Chinese corpus of multi-domain long text for entity linking . they evaluate the difficulty of documents with respect to entity linking using a measure . |
| Outcome: | The proposed corpus is based on 100 documents from diverse domains and is publicly accessible. |
Improving Candidate Retrieval with Entity Profile Generation for Wikidata Entity Linking (2022.findings-acl)
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| 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. |
Building a Multimodal Entity Linking Dataset From Tweets (2020.lrec-1)
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| Challenge: | Entity linking is a task that aims at associating an entity mention with a unique entity in a knowledge base. |
| Approach: | They propose a method to quasi-automatically build annotated datasets to evaluate methods on the Entity Linking task. |
| Outcome: | The proposed method builds annotated datasets of tweets with ambiguous mentions and a Twitter KB defining the entities. |
From Zero to Hero: Human-In-The-Loop Entity Linking in Low Resource Domains (2020.acl-main)
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| 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. |
Low-Rank Subspaces for Unsupervised Entity Linking (2021.emnlp-main)
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| Challenge: | Entity linking is an important problem with many applications. |
| Approach: | They propose a method that exploits the fact that entities that are truly mentioned in a document tend to form a semantically dense subset of all candidate entities in the document. |
| Outcome: | a new method that outperforms existing methods on real-world datasets outperformed existing methods. |
Unsupervised Entity Linking with Guided Summarization and Multiple-Choice Selection (2022.emnlp-main)
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| Challenge: | Entity linking is an important task for language understanding. |
| Approach: | They propose a fully unsupervised model that generates a guided summary of the contexts conditioning on a mention and then casts the task to a multiple-choice problem. |
| Outcome: | The proposed model achieves state-of-the-art performance on existing datasets and exiting datasets. |
SpEL: Structured Prediction for Entity Linking (2023.emnlp-main)
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| Challenge: | Entity linking is a key component of structured data creation by linking spans of text to an ontology or knowledge source. |
| Approach: | They propose to use structured prediction for entity linking to classify each input token as an entity and aggregate the token predictions. |
| Outcome: | The proposed system outperforms the state-of-the-art on the commonly used AIDA benchmark dataset for entity linking to Wikipedia. |
Revisiting Sparse Retrieval for Few-shot Entity Linking (2023.emnlp-main)
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| Challenge: | Entity linking (EL) aims to link ambiguous mentions to their corresponding entities in a knowledge base. |
| Approach: | They propose an ELECTRA-based keyword extractor to denoise the mention context and construct a better query expression. |
| Outcome: | The proposed method outperforms state-of-the-art models on the ZESHEL dataset by a significant margin. |
Real World Conversational Entity Linking Requires More Than Zero-Shots (2024.findings-acl)
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| 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. |
TweetTER: A Benchmark for Target Entity Retrieval on Twitter without Knowledge Bases (2024.lrec-main)
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| Challenge: | Entity linking is a well-established task in NLP consisting of associating entity mentions with entries in a knowledge base. |
| Approach: | They propose a benchmark that reframes entity linking as a binary entity retrieval task and uses a knowledge base to evaluate model performance. |
| Outcome: | The proposed benchmark aims to bridge the challenges in entity linking in noisy domains such as social media. |