Multi-Granularity Cross-Modality Representation Learning for Named Entity Recognition on Social Media

• URL: http://arxiv.org/abs/2210.14163v1
• Date: Wed, 19 Oct 2022 15:14:55 GMT
• Title: Multi-Granularity Cross-Modality Representation Learning for Named Entity Recognition on Social Media
• Authors: Peipei Liu, Gaosheng Wang, Hong Li, Jie Liu, Yimo Ren, Hongsong Zhu, Limin Sun
• Abstract summary: Named Entity Recognition (NER) on social media refers to discovering and classifying entities from unstructured free-form content. This work introduces the multi-granularity cross-modality representation learning. Experiments show that our proposed approach can achieve the SOTA or approximate SOTA performance on two benchmark datasets of tweets.
• Score: 11.235498285650142
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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

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