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CWI: A multimodal deep learning approach for named entity recognition from social media using character, word and image features

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Abstract

Named entity recognition (NER) from social media posts is a challenging task. User-generated content that forms the nature of social media is noisy and contains grammatical and linguistic errors. This noisy content makes tasks such as NER much harder. We propose two novel deep learning approaches utilizing multimodal deep learning and transformers. Both of our approaches use image features from short social media posts to provide better results on the NER task. On the first approach, we extract image features using InceptionV3 and use fusion to combine textual and image features. This approach presents more reliable name entity recognition when the images related to the entities are provided by the user. On the second approach, we use image features combined with text and feed it into a BERT-like transformer. The experimental results using precision, recall, and F1 score metrics show the superiority of our work compared to other state-of-the-art NER solutions.

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Notes

  1. https://developer.twitter.com/en/docs/counting-characters.

  2. Multimodal Named Entity Recognizer.

  3. A multimedia messaging application.

  4. 6 billion tokens with 200-dimensional word vectors, available at: http://nlp.stanford.edu/data/glove.6B.zip.

  5. 16 billion tokens with 300-dimensional word vectors, available at: https://dl.fbaipublicfiles.com/fastText/vectors-english/wiki-news-300d-1M.vec.zip.

  6. InceptionV3 pretrained model on ImageNet, available at: https://keras.io/applications/#inceptionv3.

  7. https://github.com/google-research/bert.

  8. BERT-Tiny: https://storage.googleapis.com/bert_models/2020_02_20/uncased_L-2_H-128_A-2.zip.

  9. BERT-Small: https://storage.googleapis.com/bert_models/2020_02_20/uncased_L-4_H-512_A-8.zip.

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Acknowledgements

The authors would like to thank Shervin Minaee for reviewing this work, and providing very useful comments to improve this work.

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Authors and Affiliations

  1. Department of Computer Engineering, University of Tabriz, Tabriz, Iran

    Meysam Asgari-Chenaghlu, M. Reza Feizi-Derakhshi, Leili Farzinvash & M. A. Balafar

  2. University of Orleans, Orleans, France

    Cina Motamed

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  1. Meysam Asgari-Chenaghlu
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Asgari-Chenaghlu, M., Feizi-Derakhshi, M.R., Farzinvash, L. et al. CWI: A multimodal deep learning approach for named entity recognition from social media using character, word and image features. Neural Comput & Applic 34, 1905–1922 (2022). https://doi.org/10.1007/s00521-021-06488-4

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