Concreteness of words has been studied extensively in psycholinguistic literature. A number of datasets has been created with average values for perceived concreteness of words. We show that we can train a regression model on these data, using word embeddings and morphological features. We evaluate the model on 7 publicly available datasets and show that concreteness and imagery values can be predicted with high accuracy. Furthermore, we analyse typical contexts of abstract and concrete words and review the potentials of concreteness prediction for image annotation.

This paper summarizes the results of a comprehensive statistical analysis on a corpus of open access articles and contained figures. It gives an insight into quantitative relationships between illustrations or types of illustrations, caption lengths, subjects, publishers, author affiliations, article citations and others.

NOA is a search engine for scientific images from open access publications based on full text indexing of all text referring to the images and filtering for disciplines and image type. Images will be annotated with Wikipedia categories for better discoverability and for uploading to WikiCommons. Currently we have indexed approximately 2,7 Million images from over 710 000 scientific papers from all fields of science.

The reuse of scientific raw data is a key demand of Open Science. In the project NOA we foster reuse of scientific images by collecting and uploading them to Wikimedia Commons. In this paper we present a text-based annotation method that proposes Wikipedia categories for open access images. The assigned categories can be used for image retrieval or to upload images to Wikimedia Commons. The annotation basically consists of two phases: extracting salient keywords and mapping these keywords to categories. The results are evaluated on a small record of open access images that were manually annotated.

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