  Mining structured knowledge from tweets using named entity recognition (NER)
can be beneficial for many down stream applications such as recommendation and
intention understanding. With tweet posts tending to be multimodal, multimodal
named entity recognition (MNER) has attracted more attention. In this paper, we
propose a novel approach, which can dynamically align the image and text
sequence and achieve the multi-level cross-modal learning to augment textual
word representation for MNER improvement. To be specific, our framework can be
split into three main stages: the first stage focuses on intra-modality
representation learning to derive the implicit global and local knowledge of
each modality, the second evaluates the relevance between the text and its
accompanying image and integrates different grained visual information based on
the relevance, the third enforces semantic refinement via iterative cross-modal
interactions and co-attention. We conduct experiments on two open datasets, and
the results and detailed analysis demonstrate the advantage of our model.
