An Element-wise Visual-enhanced BiLSTM-CRF Model for Location Name Recognition

AbstractIn recent years, previous studies have used visual information in named entity recognition (NER) for social media posts with attached images. However, these methods can only be applied to documents with attached images. In this paper, we propose a NER method that can use element-wise visual information for any documents by using image data corresponding to each word in the document. The proposed method obtains element-wise image data using an image retrieval engine, to be used as extra features in the neural NER model. Experimental results on the standard Japanese NER dataset show that the proposed method achieves a higher F1 value (89.67%) than a baseline method, demonstrating the effectiveness of using element-wise visual information.