Papers with NED
Neural Relation Extraction for Knowledge Base Enrichment (P19-1)
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| Challenge: | Existing studies focus on the extraction itself and rely on Named Entity Disambiguation (NED) to map triples into knowledge base (KB) enrichment. |
| Approach: | They propose an end-to-end relation extraction model for knowledge base enrichment based on a neural encoder-decoder model . they propose to extract entities and their relationships from sentences in the form of triples and map the elements of the extracted triples to an existing KB in an end to end manner. |
| Outcome: | The proposed model outperforms state-of-the-art baselines by 15.51% and 8.38% on two real-world datasets. |
diaNED: Time-Aware Named Entity Disambiguation for Diachronic Corpora (P18-2)
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| Challenge: | Named Entity Disambiguation (NED) systems perform well on news articles but quality drops when inputs span long time periods. |
| Approach: | They propose a time-aware method that resolves ambiguities even when mention contexts give only few cues. |
| Outcome: | The proposed method improves on a newly created diachronic corpus. |
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. |
| Approach: | They propose to use multimodal knowledge learning to link ambiguous mentions with textual and visual contexts to a predefined knowledge graph. |
| Outcome: | The proposed method achieves improvements over the state-of-the-art methods on two public MNED datasets. |
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. |
| Outcome: | The proposed method improves disambiguation accuracy on two benchmark medical NED datasets by up to 57 points. |
Improving Few-Shot Domain Transfer for Named Entity Disambiguation with Pattern Exploitation (2022.findings-emnlp)
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| Challenge: | Named entity disambiguation is a critical subtask of entity linking . a model can be trained on a domain, but it needs to be adapted to the domain . |
| Approach: | They propose to reformulate named entity disambiguation as a masked language modeling problem. |
| Outcome: | The proposed model improves on a mental health news dataset without sacrifices in accuracy. |
PICLe: Pseudo-annotations for In-Context Learning in Low-Resource Named Entity Detection (2025.naacl-long)
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| Challenge: | In-context learning is sensitive to the choice of demonstrations and can be used for tasks with few examples. |
| Approach: | They propose a framework for in-context learning with noisy, pseudo-annotated demonstrations . they annotate large quantities of demonstrations in a zero-shot first pass . |
| Outcome: | The proposed framework outperforms ICL on biomedical NED datasets with zero human-annotation. |
Unsupervised Named Entity Disambiguation for Low Resource Domains (2024.emnlp-main)
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| Challenge: | Existing approaches to Named Entity Disambiguation (NED) are inefficient for domain specific tasks such as searching, question answering and information extraction. |
| Approach: | They propose a unsupervised approach leveraging the concept of Group Steiner Trees which can identify the most relevant candidate for entity disambiguation using contextual similarities across candidate entities for all the mentions present in a document. |
| Outcome: | The proposed approach outperforms the state-of-the-art methods by more than 40% in terms of Precision@1 and Hit@5 across various domain-specific datasets. |
ShredBench: Evaluating the Semantic Reasoning Capabilities of Multimodal LLMs in Document Reconstruction (2026.findings-acl)
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Zichun Guo, Yuling Shi, Wenhao Zeng, Chao Hu, Haotian Lin, Terry Yue Zhuo, Jiawei Chen, Xiaodong Gu, Wenping Ma
| Challenge: | Empirical evaluations on state-of-the-art MLLMs reveal a significant performance gap . ML models lack the fine-grained cross-modal reasoning required to bridge visual discontinuities. |
| Approach: | They propose a benchmark that renders fragmented documents directly from Markdown to facilitate evaluation of VRDU tasks. |
| Outcome: | The proposed benchmark renders fragmented documents directly from Markdown. |