  Named Entity Recognition (NER) on social media refers to discovering and
classifying entities from unstructured free-form content, and it plays an
important role for various applications such as intention understanding and
user recommendation. With social media posts tending to be multimodal,
Multimodal Named Entity Recognition (MNER) for the text with its accompanying
image is attracting more and more attention since some textual components can
only be understood in combination with visual information. However, there are
two drawbacks in existing approaches: 1) Meanings of the text and its
accompanying image do not match always, so the text information still plays a
major role. However, social media posts are usually shorter and more informal
compared with other normal contents, which easily causes incomplete semantic
description and the data sparsity problem. 2) Although the visual
representations of whole images or objects are already used, existing methods
ignore either fine-grained semantic correspondence between objects in images
and words in text or the objective fact that there are misleading objects or no
objects in some images. In this work, we solve the above two problems by
introducing the multi-granularity cross-modality representation learning. To
resolve the first problem, we enhance the representation by semantic
augmentation for each word in text. As for the second issue, we perform the
cross-modality semantic interaction between text and vision at the different
vision granularity to get the most effective multimodal guidance representation
for every word. Experiments show that our proposed approach can achieve the
SOTA or approximate SOTA performance on two benchmark datasets of tweets. The
code, data and the best performing models are available at
https://github.com/LiuPeiP-CS/IIE4MNER
