FRCL-MNER: A Finer Grained Rank-Based Contrastive Learning Framework for Multimodal NER

Area of Science:

• Natural Language Processing
• Computer Vision
• Machine Learning

Background:

• Multimodal Named Entity Recognition (MNER) integrates text and image data for entity detection.
• Existing MNER methods often miss fine-grained details and suffer from error propagation due to pre-detection steps.
• There is a need for advanced techniques to capture richer multimodal interactions.

Purpose of the Study:

• To propose a novel framework, finer grained rank-based contrastive learning (FRCL), for MNER.
• To address limitations of previous MNER approaches by focusing on finer-grained information within modalities.
• To improve the accuracy and robustness of multimodal named entity recognition.

Main Methods:

• Developed a FRCL framework utilizing global-level contrastive learning to align semantic features across modalities.
• Implemented a Top-K rank-based mask strategy to construct effective positive-negative pairs for contrastive learning.
• Focused on learning finer-grained multimodal interaction representations beyond simple feature fusion.

Main Results:

• The FRCL framework demonstrated superior performance compared to existing strong baselines on three social media datasets.
• Achieved a notable improvement of up to 1.54% on the Twitter2015 dataset.
• Experimental results and discussions confirmed the effectiveness and robustness of the proposed approach.

Conclusions:

• The proposed FRCL framework significantly advances the state-of-the-art in MNER.
• Fine-grained multimodal interaction learning is crucial for improving entity recognition accuracy.
• The approach offers a promising direction for future research in multimodal AI.