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

Multimodal named entity recognition (MNER) is an emerging field that aims to automatically detect named entities and classify their categories, utilizing input text and auxiliary resources such as images. While previous studies have leveraged object detectors to preprocess images and fuse textual semantics with corresponding image features, these methods often overlook the potential finer grained information within each modality and may exacerbate error propagation due to predetection. To address these issues, we propose a finer grained rank-based contrastive learning (FRCL) framework for MNER.

Hierarchical Label-Enhanced Contrastive Learning for Chinese NER.

Recently, character-word lattice structures have achieved promising results for Chinese named entity recognition (NER), reducing word segmentation errors and increasing word boundary information for character sequences. However, constructing the lattice structure is complex and time-consuming, thus these lattice-based models usually suffer from low inference speed. Moreover, the quality of the lexicon affects the accuracy of the NER model.