PCEN: Potential Correlation-Enhanced Network for Multimodal Named Entity Recognition

Jiakai Geng, Chenyang Zhang, Linjing Li, Qing Yang, Daniel Dajun Zeng

Abstract: Multimodal Named Entity Recognition (MNER) in social media posts plays an important role in both security and natural language processing domains. Existing approaches mainly include extracting useful visual features from images, and integrating them into text representation for NER via multimodal fusion. Nevertheless, there is potential correlation among samples in the dataset, but is ignored by most of the existing studies. In this paper, we propose a potential correlation-enhanced network (PCEN) for MNER. Specifically, we (1) consider the potential correlation as an important visual feature for MNER, and (2) utilize it to guide the final recognition of entities. To tackle the first issue, we employ unsupervised clustering to divide the images of training samples into clusters, and take the trainable embedding of each cluster label as a visual feature because samples with the same cluster label have higher potential correlation. To tackle the second issue, we argue that the samples in the same cluster are more likely to have similar distributions of entity types in their text. We design an inconsistency loss to encourage the consistency between the entity recognition result of each sample and the pre-trained entity type distribution of the corresponding cluster this sample belongs to. Experiments on two MNER benchmarks demonstrate the effectiveness of our proposed method.

External IDs:dblp:conf/isi/GengZLYZ23