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Multimodal Named Entity Recognition for Short Social Media Posts

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Academic year: 2020

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Figure 1:Multimodal NER + modality attention.(a) Visual contexts help recognizing polysemous entitynames (‘Monopoly’ as in a board game versus an eco-nomics term)
Figure 2: The main architecture for our multimodalNER (MNER) network with modality attention.Ateach decoding step, word embeddings, character em-beddings, and visual features are merged with modalityattention
Table 2: Error analysis: when do images help NERvision input (W+C+V) and the one without (W+C) for the underlined named entities (or false positives) are shown.? Ground-truth labels (GT) and predictions of our model withFor interpretability, visual tags (label output of InceptionNet) are presented instead of actual feature vectors used.
Table 3: NER performance (F1) on SnapCaptions withvarying word embeddings vocabulary sizeels being compared:.Mod-(W+C) Bi-LSTM/CRF + Bi-CharLSTM w/ and w/o modality attention (M.A.)

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