| Challenge: | Effectively using an entity mention's context to disambiguate it is the crux of the entity linking task. |
| Approach: | They propose to use convolutional neural networks to extract cues from context to effectively disambiguate between closely related concepts. |
| Outcome: | The proposed model outperforms previous work on the WikilinksNED test set by 2.8% absolute. |
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Contextual Augmentation for Entity Linking using Large Language Models (2025.coling-main)
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| 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. |
Improving Fine-grained Entity Typing with Entity Linking (D19-1)
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| 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. |
Distant Learning for Entity Linking with Automatic Noise Detection (P19-1)
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| 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. |
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. |
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. |
Boosting Entity Linking Performance by Leveraging Unlabeled Documents (P19-1)
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| 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. |
Learning from Context or Names? An Empirical Study on Neural Relation Extraction (2020.emnlp-main)
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| Challenge: | Existing datasets may leak shallow heuristics via entity mentions, thus contributing to the high performance on RE benchmarks. |
| Approach: | They propose an entity-masked contrastive framework for relation extraction to gain a deeper understanding on textual context and type information while avoiding rote memorization of entities. |
| Outcome: | The proposed framework improves the effectiveness and robustness of neural models in different RE scenarios. |
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. |
Improving Entity Disambiguation by Reasoning over a Knowledge Base (2022.naacl-main)
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| Challenge: | Recent work in entity disambiguation relies on a limited subset of KB facts to link entities . less common entities are prone to missing or inconsistent KB information, which is problematic for models which rely on 'one source' |
| Approach: | They propose an ED model which links entities by reasoning over a symbolic knowledge base in a fully differentiable fashion. |
| Outcome: | The proposed model outperforms state-of-the-art models on six well-established datasets by 1.3 F1 on average. |
The Role of Global and Local Context in Named Entity Recognition (2023.acl-short)
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| Challenge: | Named Entity Recognition (NER) models are usually applied sequentially because of their complexity. |
| Approach: | They explore the impact of global document context on Named Entity Recognition . they find that correctly retrieving global document contextual has a greater impact . |
| Outcome: | The proposed model can retrieve global context better than leveraging local context . authors say the model can push the state of the art further . |