  Content-based information retrieval is based on the information contained in
documents rather than using metadata such as keywords. Most information
retrieval methods are either based on text or image. In this paper, we
investigate the usefulness of multimodal features for cross-lingual news search
in various domains: politics, health, environment, sport, and finance. To this
end, we consider five feature types for image and text and compare the
performance of the retrieval system using different combinations. Experimental
results show that retrieval results can be improved when considering both
visual and textual information. In addition, it is observed that among textual
features entity overlap outperforms word embeddings, while geolocation
embeddings achieve better performance among visual features in the retrieval
task.
