AgeSuitability Prediction for Literature Using a Recurrent Neural Network Model Digital media holds a strong presence in society today. Providers of digital media may choose to have their content undergo an agesuitability analysis before being published. These analyses provide ratings that denote to which age group(s) a particular media item is appropriate. Content rating systems exist in many countries for television, music, video games, and mobile applications. These systems allow consumers to quickly determine whether or not a given media item is suitable to their age or preference. Literature, on the other hand, remains devoid of a comparable rating system. If a new, humandriven rating system for literature were to be implemented, it would be impeded by the fact that literary content is produced far more rapidly than are other forms of digital media; human working within such a system simply would not be able to read and analyze literature at its current rate of production. Thus, to provide fast, automated agesuitability ratings to works of literature (i.e., books), we propose a computerdriven rating system which predicts a booku0027s content rating within each of eight categories: (a) crude humorlanguage; (b) drug, alcohol, and tobacco use; (c) kissing; (d) profanity; (e) nudity; (f) sex and intimacy; (g) violence and horror; and (h) gaylesbian characters given the text of that book. Our computerdriven system circumvents the major hindrance to any theoretical humandriven rating system previously mentioned, i.e., infeasibility in time spent.

Article Segmentation in Digitised Newspapers with a 2D Markov Model Document analysis and recognition is increasingly used to digitise collections of historical books, newspapers and other periodicals. In the digital humanities, it is often the goal to apply information retrieval (IR) and natural language processing (NLP) techniques to help researchers analyse and navigate these digitised archives. The lack of article segmentation is impairing many IR and NLP systems, which assume text is split into ordered, errorfree documents. We define a document analysis and image processing task for segmenting digitised newspapers into articles and other content, e.g. adverts, and we automatically create a dataset of 11602 articles. Using this dataset, we develop and evaluate an innovative 2D Markov model that encodes reading order and substantially outperforms the current stateoftheart, reaching similar accuracy to human annotators.