| Challenge: | Existing methods for named entity disambiguation are weak for short text . performance of existing methods drops dramatically for short texts . |
| Approach: | They propose a knowledge-enhanced method for named entity disambiguation . they use factual knowledge graph and conceptual knowledge graph to provide additional knowledge . |
| Outcome: | The proposed method achieves significant improvement on a large manually annotated short-text dataset and the state-of-the-art on three standard datasets. |
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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' |
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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. |
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Medical Entity Disambiguation with Medical Mention Relation and Fine-grained Entity Knowledge (2024.lrec-main)
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| Challenge: | Existing methods for medical entity disambiguation (MED) fail to fully utilize the knowledge within medical knowledge bases (KBs) Existing models overlook essential interactions between medical mentions and candidate entities, resulting in knowledge- and interaction-inefficient modeling and suboptimal disambiguations performance. |
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Improving Neural Entity Disambiguation with Graph Embeddings (P19-2)
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| Challenge: | Entity Disambiguation (ED) is the task of linking an ambiguous entity mention to a corresponding entry in a knowledge base. |
| Approach: | They propose a method that integrates structured information from the knowledge base with unstructured information from text-based representations. |
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Multimodal Knowledge Learning for Named Entity Disambiguation (2022.findings-emnlp)
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| Challenge: | Existing attempts to model multimodal information at the knowledge level are lacking multimodal annotation data against the large-scale unlabeled corpus. |
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Entity Disambiguation with Entity Definitions (2023.eacl-main)
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| Challenge: | Entity Disambiguation (ED) is a crucial problem in Natural Language Processing (NLP). |
| Approach: | They propose to use Wikipedia titles as the textual representation of each candidate to improve the generalization capability over unseen patterns. |
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RAED: Retrieval-Augmented Entity Description Generation for Emerging Entity Linking and Disambiguation (2025.emnlp-main)
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| Challenge: | Entity Linking and Entity Disambiguation systems assume static knowledge bases are incomplete and up-to-date, rendering them incapable of handling entities not yet included in the knowledge base. |
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Unsupervised Named Entity Disambiguation for Low Resource Domains (2024.emnlp-main)
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An Analysis of Simple Data Augmentation for Named Entity Recognition (2020.coling-main)
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| Challenge: | Recent studies have focused on using data augmentation techniques on sentence-level and sentence-pair natural language processing tasks such as text classification. |
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Cross-Domain Data Integration for Named Entity Disambiguation in Biomedical Text (2021.findings-emnlp)
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| Challenge: | Existing methods for named entity disambiguation are limited by coarse-grained structural resources in biomedical knowledge bases and training datasets that provide low coverage over uncommon resources. |
| Approach: | They propose a method that integrates structural knowledge from general text knowledge bases to the medical domain. |
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