Transition-based and graph-based dependency parsers have previously been shown to have complementary strengths and weaknesses: transition-based parsers exploit rich structural features but suffer from error propagation, while graph-based parsers benefit from global optimization but have restricted feature scope. In this paper, we show that, even though some details of the picture have changed after the switch to neural networks and continuous representations, the basic trade-off between rich features and global optimization remains essentially the same. Moreover, we show that deep contextualized word embeddings, which allow parsers to pack information about global sentence structure into local feature representations, benefit transition-based parsers more than graph-based parsers, making the two approaches virtually equivalent in terms of both accuracy and error profile. We argue that the reason is that these representations help prevent search errors and thereby allow transitionbased parsers to better exploit their inherent strength of making accurate local decisions. We support this explanation by an error analysis of parsing experiments on 13 languages.

National differences in uncertainty, inequality, and trust have been accentuated by COVID-19. There are indications that the pandemic has impacted societies characterized by high uncertainty, inequality, and low trust harder than societies characterized by low uncertainty, equality, and high trust. This study investigates differential response strategies to COVID-19 as reflected in news media of two otherwise similar low uncertainty societies: Denmark and Sweden. The comparison is made using a recent approach to information dynamics in unstructured data. The main findings are that the news dynamics generally mirror public-health policies, capture fundamental socio-cultural variables related to uncertainty and trust, and may provide a measure of societal uncertainty. The findings can provide insights into evolutionary trajectories of decision-making under high uncertainty and, from a methodological level, be used to develop a media-based index of uncertainty and trust.

How can novel AI techniques be made and put to use in the library? Combining methods from data and library science, this article focuses on Natural Language Processing technologies, especially in national libraries. It explains how the National Library of Sweden's collections enabled the development of a new BERT language model for Swedish. It also outlines specific use cases for the model in the context of academic libraries, detailing strategies for how such a model could make digital collections available for new forms of research, from automated classification to enhanced searchability and improved OCR cohesion. Highlighting the potential for cross-fertilizing AI with libraries, the conclusion suggests that while AI may transform the workings of the library, libraries can also play a key role in the future development of AI.

How can novel AI techniques be made and put to use in the library? Combining methods from data and library science, this article focuses on Natural Language Processing technologies in especially national libraries. It explains how the National Library of Sweden’s collections enabled the development of a new BERT language model for Swedish. It also outlines specific use cases for the model in the context of academic libraries, detailing strategies for how such a model could make digital collections available for new forms of research: from automated classification to enhanced searchability and improved OCR cohesion. Highlighting the potential for cross-fertilizing AI with libraries, the conclusion suggests that while AI may transform the workings of the library, libraries can also have a key role to play in the future development of AI.

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