Challenge: Existing methods for named entity recognition on social media are not efficient for semi-supervised MNER because of the mismatch between the posted text and image.
Approach: They propose a novel method to fuse the text and image features for multimodal named entity recognition under semi-supervised setting by exploiting modal-specific VAEs.
Outcome: The proposed method outperforms baselines under supervised setting and improves performance with less labeled data than existing semi-supervised methods.

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An Effective Span-based Multimodal Named Entity Recognition with Consistent Cross-Modal Alignment (2024.lrec-main)

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Challenge: Existing approaches to name entity recognition rely on word-based sequence labeling and align image and text at inconsistent semantic levels.
Approach: They propose a span-based method which achieves a more consistent multimodal alignment from the perspectives of information-theoretic and cross-modal interaction.
Outcome: Experiments on two datasets show that SMNER outperforms the state-of-the-art methods.
Multimodal Named Entity Recognition for Short Social Media Posts (N18-1)

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Challenge: Social media posts often contain inconsistent or incomplete syntax and lexical notations with limited textual contexts.
Approach: They propose a task called Multimodal Named Entity Recognition (MNER) for noisy user-generated data . they use a dataset called SnapCaptions to build upon the state-of-the-art NER models .
Outcome: The proposed model outperforms existing models on noisy user-generated data . it uses a deep image network and generic modality attention module .
Improving Multimodal Named Entity Recognition via Entity Span Detection with Unified Multimodal Transformer (2020.acl-main)

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Challenge: Existing methods for named entity recognition ignore visual context bias . NER is a key component of many information extraction tasks .
Approach: They propose to use a multimodal interaction module to generate word-aware visual representations and leverage purely text-based entity span detection as an auxiliary module to guide the final predictions.
Outcome: The proposed approach achieves state-of-the-art on two benchmark datasets.
MNER-MI: A Multi-image Dataset for Multimodal Named Entity Recognition in Social Media (2024.lrec-main)

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Challenge: Recent research has focused on multimodal named entity recognition (MNER) but current approaches focus on text and a single accompanying image, leaving a significant research gap in multi-image scenarios.
Approach: They propose to construct a human-annotated MNER dataset with multiple images called MNER-MI and a temporal prompt model with multiple image to address the new challenges in multi-image scenarios.
Outcome: The proposed method achieves state-of-the-art results on both MNER-MI and MNER -MI-Plus, demonstrating its effectiveness.
Flat Multi-modal Interaction Transformer for Named Entity Recognition (2022.coling-1)

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Challenge: Named entity recognition (MNER) aims at identifying entity spans and recognizing their categories in social media posts with the aid of images.
Approach: They propose to use sentences and general domain words to obtain visual cues to transform the fine-grained semantic representation of vision and text into a unified lattice structure and leverage entity boundary detection as an auxiliary task to alleviate visual bias.
Outcome: The proposed method achieves state-of-the-art on two benchmark datasets.
Enhancing Multimodal Named Entity Recognition through Adaptive Mixup Image Augmentation (2025.coling-main)

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Challenge: Current named entity recognition methods struggle with text-image mismatch problem due to a lack of visual context.
Approach: They propose an adaptive mixup image augmentation method that generates augmented images based on matching score between text and image .
Outcome: The proposed method can be integrated into existing models and demonstrate consistent performance improvements.
Enhancing NER by Harnessing Multiple Datasets with Conditional Variational Autoencoders (2025.acl-short)

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Challenge: Named Entity Recognition (NER) is a fundamental NLP task . supervised learning or full fine-tuning remains essential for high performance NER models.
Approach: They propose to integrate CVAE into a span-based Named Entity Recognition model.
Outcome: The proposed method achieves better performance on the BioRED dataset.
ESPVR: Entity Spans Position Visual Regions for Multimodal Named Entity Recognition (2023.findings-emnlp)

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Challenge: Existing methods for acquiring local visual information are limited . existing methods for named entity recognition are redundant or insufficient .
Approach: They propose an Entity Spans Position Visual Regions module to obtain visual regions corresponding to entities in the text.
Outcome: The proposed method achieves the SOTA on Twitter-2017 and competitive results on Twitter 2015 . previous efforts have yielded promising results, but they still fall short in selecting visual information.
Capturing Latent Modal Association For Multimodal Entity Alignment (2025.findings-emnlp)

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Challenge: Existing methods for multimodal entity alignment overlook the quality of input modality embeddings during modality interaction, amplifying noise propagation while suppressing discriminative feature representations.
Approach: They propose a model for capturing latent modal association for multimodal entity alignment using a self-attention mechanism to enhance salient information while attenuating noise within individual modality embeddings.
Outcome: The proposed model achieves an absolute 3.1% higher Hits@1 score than the sota method.
Breaking the Boundaries: A Unified Framework for Chinese Named Entity Recognition Across Text and Speech (2024.findings-emnlp)

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Challenge: Existing approaches to Named Entity Recognition (NER) tasks are limited by the complexity of the data and the potential connections between tasks.
Approach: They propose a task to break the boundaries between different modal NER tasks by using a unified data format for inputs from different modalités.
Outcome: The proposed task breaks the boundaries between different modal NER tasks and is a unified implementation of them.

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