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On development of multimodal named entity recognition using part-of-speech and mixture of experts

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Abstract

Multimodal Named Entity Recognition (MNER) is a fundamental task in the field of natural language processing for social media posts. Current MNER models fail to deal with the relation between text and image entities, which results in the textual noise, image noise and even multimodal noise during processing. In this paper, we first introduce the Part-of-speech (POS) information, which is used for non-entity words eliminating and textual noise filtering. A POS-base gated cross-modal attention network is established to precisely learn the textual and visual representations to remove the image noise. Then, a Mixture-of-Experts (MOE) is proposed for multimodality integration, which optimize the effectiveness of named entity identification and filter the multimodal noise. We evaluate the proposed model on the Twitter dataset and the experimental results establish a strong evidence of the state-of-the-art performance.

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Funding

This work was supported by the National Statistical Science Research Project of China under Grant No. 2016LY98, the Characteristic Innovation Projects of Guangdong Colleges and Universities (Nos. 2018KTSCX049), the Science and Technology Plan Project of Guangzhou under Grant Nos. 202102080258 and 201903010013.

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Authors and Affiliations

  1. Guangdong Provincial Key Laboratory of Quantum Engineering and Quantum Materials, School of Physics and Tele-Communication Engineering, South China Normal University, Guangzhou, 510006, China

    Jianying Chen

  2. NIT, Zhejiang University, Zhejiang, 310058, China

    Haolan Zhang

  3. School of Information Science and Technology, Nantong University, Nantong, 226019, Nantong, China

    Weiping Ding

  4. School of Electronics and Information Engineering, South China Normal University, 528225, Foshan, China

    Yun Xue, Zhengxuan Zhang & Jiehai Chen

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  1. Jianying Chen
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Correspondence to Yun Xue.

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Chen, J., Xue, Y., Zhang, H. et al. On development of multimodal named entity recognition using part-of-speech and mixture of experts. Int. J. Mach. Learn. & Cyber. 14, 2181–2192 (2023). https://doi.org/10.1007/s13042-022-01754-w

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  • DOI: https://doi.org/10.1007/s13042-022-01754-w

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