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20222026
most citedPrompt Me Up: Unleashing the Power of Alignments for Multimodal Entity and Relation Extraction

2 citations · 2 across the 7 of their papers we have counts for

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cs.CL2026

Anchored Sliding Window: Toward Robust and Imperceptible Linguistic Steganography

Ruiyi Yan, Shiao Meng, Yugo Murawaki

Linguistic steganography based on language models typically assumes that steganographic texts are transmitted without alteration, making them fragile to even minor modifications. W…

cs.CL2025

GenCNER: A Generative Framework for Continual Named Entity Recognition

Yawen Yang, Fukun Ma, Shiao Meng +2

Traditional named entity recognition (NER) aims to identify text mentions into pre-defined entity types. Continual Named Entity Recognition (CNER) is introduced since entity catego…

cs.CL2025

GapDNER: A Gap-Aware Grid Tagging Model for Discontinuous Named Entity Recognition

Yawen Yang, Fukun Ma, Shiao Meng +2

In biomedical fields, one named entity may consist of a series of non-adjacent tokens and overlap with other entities. Previous methods recognize discontinuous entities by connecti…

cs.CL2024

On the Robustness of Document-Level Relation Extraction Models to Entity Name Variations

Shiao Meng, Xuming Hu, Aiwei Liu +4

Driven by the demand for cross-sentence and large-scale relation extraction, document-level relation extraction (DocRE) has attracted increasing research interest. Despite the cont…

cs.CL20232 cited

Prompt Me Up: Unleashing the Power of Alignments for Multimodal Entity and Relation Extraction

Xuming Hu, Junzhe Chen, Aiwei Liu +3

How can we better extract entities and relations from text? Using multimodal extraction with images and text obtains more signals for entities and relations, and aligns them throug…

cs.CL2023

RAPL: A Relation-Aware Prototype Learning Approach for Few-Shot Document-Level Relation Extraction

Shiao Meng, Xuming Hu, Aiwei Liu +4

How to identify semantic relations among entities in a document when only a few labeled documents are available? Few-shot document-level relation extraction (FSDLRE) is crucial for…