HOSMEL: A Hot-Swappable Modularized Entity Linking Toolkit for Chinese (2022.acl-demo)
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| Challenge: | Existing studies have explored the use of entity linking (EL) in downstream tasks. |
| Approach: | They propose a modularized entity linking toolkit for easy task adaptation. |
| Outcome: | The proposed toolkit achieves significantly better accuracy and less time and spaceconsumption than existing methods. |
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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. |
Multi-lingual Entity Discovery and Linking (P18-5)
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| Challenge: | This tutorial reviews the framework of cross-lingual EL and motivates it as a broad paradigm for the Information Extraction task. |
| Approach: | This tutorial will review the framework of cross-lingual EL and motivate it as a broad paradigm for the Information Extraction task. |
| Outcome: | The aim of this tutorial is to review the framework of cross-lingual EL and motivate it as a broad paradigm for the Information Extraction 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. |
Contextualized End-to-End Neural Entity Linking (2020.aacl-main)
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| 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. |
Instructed Language Models with Retrievers Are Powerful Entity Linkers (2023.emnlp-main)
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| 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. |
ChatEL: Entity Linking with Chatbots (2024.lrec-main)
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| 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%. |
mReFinED: An Efficient End-to-End Multilingual Entity Linking System (2023.findings-emnlp)
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| Challenge: | Existing work assumed that entity mentions were given and skipped the entity mention detection step due to a lack of high-quality multilingual training corpora. |
| Approach: | They propose a bootstrapping mention detection framework that enhances the quality of training corpora. |
| Outcome: | The proposed framework outperforms existing work in the end-to-end MEL task while being 44 times faster. |
Fine-Grained Evaluation for Entity Linking (D19-1)
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| 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. |
Named Entity Recognition for Entity Linking: What Works and What’s Next (2021.findings-emnlp)
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| 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. |
Entity Linking in 100 Languages (2020.emnlp-main)
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| Challenge: | Existing approaches to multilingual entity linking are cross-lingual, with a focus on zero-shot evaluation. |
| Approach: | They propose a new formulation for multilingual entity linking where language-specific mentions resolve to a language-agnostic Knowledge Base. |
| Outcome: | The proposed model outperforms state-of-the-art models on a large multilingual dataset and shows that frequency-based analysis provided key insights for the model and training enhancements. |