In 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.

In this paper, we propose structure transformation rules for statistical machine translation which are lexicalized by only function words. Although such rules can be extracted from an aligned parallel corpus simply as original phrase pairs, their structure is hierarchical and thus can be used in a hierarchical translation system. In addition, structure transformation rules can take into account long-distance reordering, allowing for more than two phrases to be moved simultaneously. The rule set is used as a core module in our hierarchical model together with two other modules, namely, a basic reordering module and an optional gap phrase module. Our model is considerably more compact and produces slightly higher BLEU scores than the original hierarchical phrase-based model in Japanese-English translation on the parallel corpus of the NTCIR-7 patent translation task.