| Challenge: | Entity linking aims to link entity mentions in texts to knowledge bases, but existing methods rely on local contexts to resolve entities independently. |
| Approach: | They propose a neural model for collective entity linking that integrates local contextual features and global coherence information to improve the computation efficiency. |
| Outcome: | The proposed model improves its performance on five publicly available datasets and can be used to train on Wikipedia hyperlinks to avoid overfitting and domain bias. |
Similar Papers
Improving Entity Linking by Modeling Latent Relations between Mentions (P18-1)
Copied to clipboard
| 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. |
BERT might be Overkill: A Tiny but Effective Biomedical Entity Linker based on Residual Convolutional Neural Networks (2021.findings-emnlp)
Copied to clipboard
| Challenge: | Biomedical entity linking is a task of linking entities in biomedical documents to referent entities in a knowledge base. |
| Approach: | They propose an efficient convolutional neural network with residual connections for biomedical entity linking. |
| Outcome: | The proposed model achieves comparable or even better linking accuracy on five public datasets while having about 60 times fewer parameters. |
Distant Learning for Entity Linking with Automatic Noise Detection (P19-1)
Copied to clipboard
| Challenge: | Accurate entity linkers have been produced for domains and languages where no or very limited amounts of labeled data are available. |
| Approach: | They propose to use annotated text to learn to link entities without labeling . they frame the task as a multi-instance learning problem and rely on surface matching to create initial noisy labels. |
| Outcome: | The proposed method outperforms the baseline surface matching model for a subset of entities. |
Learning Dynamic Context Augmentation for Global Entity Linking (D19-1)
Copied to clipboard
Xiyuan Yang, Xiaotao Gu, Sheng Lin, Siliang Tang, Yueting Zhuang, Fei Wu, Zhigang Chen, Guoping Hu, Xiang Ren
| 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. |
MOLEMAN: Mention-Only Linking of Entities with a Mention Annotation Network (2021.acl-short)
Copied to clipboard
| Challenge: | Existing approaches to entity linking represent each entity with a single vector, but instead use a contextualized mention-encoder that learns to place similar mentions of the same entity closer in vector space than mentions from different entities. |
| Approach: | They propose an instance-based nearest neighbor approach to entity linking that allows for a contextualized mention-encoder to learn to place similar mentions of the same entity closer in vector space than mentions from different entities. |
| Outcome: | The proposed approach outperforms all other systems on two multilingual benchmarks and is simpler to train and interpretable. |
Contextual Augmentation for Entity Linking using Large Language Models (2025.coling-main)
Copied to clipboard
| Challenge: | Entity Linking involves detecting and linking entity mentions in natural language texts to a knowledge graph. |
| Approach: | They propose a fine-tuned model that integrates entity recognition and disambiguation in a unified framework. |
| Outcome: | The proposed model achieves state-of-the-art on out-of domain datasets and compares with baselines. |
SpEL: Structured Prediction for Entity Linking (2023.emnlp-main)
Copied to clipboard
| 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. |
Boosting Entity Linking Performance by Leveraging Unlabeled Documents (P19-1)
Copied to clipboard
| Challenge: | a new approach to entity linking relies on unlabeled documents and Wikipedia . a supervised approach uses only natural information, such as unlabed documents . |
| Approach: | They propose a method which exploits only naturally occurring information . they construct a high recall list of candidate entities for each mention in an unlabeled document . |
| Outcome: | The proposed model outperforms fully-supervised state-of-the-art systems on standard test sets. |
Explicitly Capturing Relations between Entity Mentions via Graph Neural Networks for Domain-specific Named Entity Recognition (2021.acl-short)
Copied to clipboard
| Challenge: | Named entity recognition (NER) is well studied for the general domain, but the performance is still moderate for specialized domains. |
| Approach: | They propose to explicitly connect entity mentions based on global coreference relations and local dependency relations to build better entity mention representations. |
| Outcome: | The proposed system improves the NER performance even with a tiny amount of labeled data. |
Improving Fine-grained Entity Typing with Entity Linking (D19-1)
Copied to clipboard
| Challenge: | Existing methods for fine-grained entity typing require a large tag set and knowledge of the context. |
| Approach: | They propose a deep neural model that uses context and information from entity linking to improve fine-grained entity typing. |
| Outcome: | The proposed model achieves 5% absolute strict accuracy improvement over the state of the art on two datasets. |