We report on a case study of text and images that reveals the inadequacy of simplistic assumptions about their connection and interplay. The context of our work is a larger effort to create automatic systems that can extract event information from online news articles about flooding disasters. We carry out a manual analysis of 1000 articles containing a keyword related to flooding. The analysis reveals that the articles in our data set cluster into seven categories related to different topical aspects of flooding, and that the images accompanying the articles cluster into five categories related to the content they depict. The results demonstrate that flood-related news articles do not consistently report on a single, currently unfolding flooding event and we should also not assume that a flood-related image will directly relate to a flooding-event described in the corresponding article. In particular, spatiotemporal distance is important. We validate the manual analysis with an automatic classifier demonstrating the technical feasibility of multimedia analysis approaches that admit more realistic relationships between text and images. In sum, our case study confirms that closer attention to the connection between text and images has the potential to improve the collection of multimodal information from news articles.

This paper presents our submissions to SemEval-2019 Task9, Suggestion Mining. Our system is one in a series of systems in which we compare an approach using expert-defined rules with a comparable one using machine learning. We target tasks with a syntactic or semantic component that might be better described by a human understanding the task than by a machine learner only able to count features. For Semeval-2019 Task 9, the expert rules clearly outperformed our machine learning model when training and testing on equally balanced testsets.

With the goal of discovering differences between Belgian and Netherlandic Dutch, we participated as Team Taurus in the Dutch-Flemish Subtitles task of VarDial2018. We used a rather simple marker-based method, but a wide range of features, including lexical, lexico-syntactic and syntactic ones, and achieved a second position in the ranking. Inspection of highly distin-guishing features did point towards differences between the two language varieties, but because of the nature of the experimental data, we have to treat our observations as very tentative and in need of further investigation.