Aiding Intra-Text Representations with Visual Context for Multimodal Named Entity Recognition
TABLE II: Comparison of our approach with baselines and previous state-of-the-art methods.
['T-NER ([11)', 'PER F1 83.64', 'LOC F1 76.18', 'ORG F1 50.26', 'MISC F1 34.56', 'Overall Prec. Recall F1 69.54', 'Overall Prec. Recall F1 68.65', 'Overall Prec. Recall F1 69.09']
We achieved best F1 score, outperforming previous approaches. Twitter Embeddings To compare our results with this work, we used same embeddings to evaluate the improvement that comes from our proposed architecture. Our model achieves better results using Twitter Embedding, but there is a clear performance boost with Crawl embeddings. It is interesting to note that entities “PER” and “LOC” have a minor improvement whereas there is an enhancement of 5-6% in “ORG” and “MISC”. With Crawl embeddings, our model performs best to predict the “ORG” category, with a 3% difference from baseline (text only using the Disan model). Our model achieves best F1 scores using both embeddings. This clearly shows that our model performs better regardless to the word embedding. Given an annotated sentence: