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The rise of the digital society is accompanied by incalculable social risks, but very little IS research has examined the implications of the new digital society. Drawing on concepts from Beck’s critical theory of the risk society and critical discourse analysis, this study examines the public discourse on risk events during the launch of NemID, a personal digital identifier for Danish citizens. This research illustrates our difficulties and challenges in managing some of the fundamental social risks from societal digitalisation. Limited institutional capabilities for digital technologies force public officials to depend on private companies motived by profit instead of the public interest. Beliefs in digital technology as the primary determinant of social and economic progress also present many public management dilemmas. When digital risk events occur and citizens’ fears are stoked by news media and public discourse, public officials seem to have no other strategy for managing the escalating fears than systematically distorted communication. The continued rise of the digital risk society demands that IS research respond to the challenge of generating knowledge for its public management.

This report describes a new research resource: a searchable database of 4,700 naturally occurring instances of sluicing in English, annotated so as to shed light on the questions that have shaped research on ellipsis since the 1960s. The paper describes the data set and how it can be obtained, how it was constructed, how it is organized, and how it can be queried. It also highlights some initial empirical findings, first describing general characteristics of the data, then focusing more closely on issues concerning antecedents and possible mismatches between antecedents and ellipsis sites.

The literature on ellipsis is notable for its contentiousness and diversity of viewpoints. We argue, however, that this literature can be viewed as an orderly series of insights, beginning with the notion of abstract identity of the Sag–Williams account, and moving ultimately to a view of ellipsis as a discourse phenomenon.

We apply Natural Language Processing and super-vised Machine Learning to predict stock price movements, based on approximately 30,000 ad-hoc disclosures issued by publicly traded companies on Nasdaq OMX Stockholm. Three different labeling methods, based on financial theory, are defined and assessed. The best results, using Logistic Regression and TF-IDF with character-grams, achieve an increase of 6,3 percentage points above a majority class baseline. These results show that corporate ad-hoc disclosures, which are regulated to represent novel and value-relevant information, are particularly well-suited for this task. Furthermore, the most sophisticated labeling technique used, Jensen’s Alpha in the context of the Capital Asset Pricing Model, helps the model achieve its highest accuracy. The results therefore show that financial theory can help isolate the effect of an informational event on stock prices, improving the supervised Machine Learning approach. Finally, an algorithmic trading strategy is simulated with the best model, yielding positive abnormal returns.

Most, if not all forms of ellipsis (e.g., so does Mary) are similar to reading comprehension questions (what does Mary do), in that in order to resolve them, we need to identify an appropriate text span in the preceding discourse. Following this observation, we present an alternative approach for English ellipsis resolution relying on architectures developed for question answering (QA). We present both single-task models, and joint models trained on auxiliary QA and coreference resolution datasets, clearly outperforming the current state of the art for Sluice Ellipsis (from 70.00 to 86.01 F1) and Verb Phrase Ellipsis (from 72.89 to 78.66 F1)., To appear in EACL 2021

In this article we develop a novel online framework to visualize news data over a time horizon. First, we perform a Natural Language Processing analysis, wherein the words are extracted, and their attributes, namely the importance and the relatedness, are calculated. Second, we present a Mathematical Optimization model for the visualization problem and a numerical optimization approach. The model represents the words using circles, the time-varying area of which displays the importance of the words in each time period. Word location in the visualization region is guided by three criteria, namely, the accurate representation of semantic relatedness, the spread of the words in the visualization region to improve the quality of the visualization, and the visual stability over the time horizon. Our approach is flexible, allowing the user to interact with the display, as well as incremental and scalable. We show results for three case studies using data from Danish news sources.

Sluice resolution in English is the problem of finding antecedents of wh-fronted ellipses. Previous work has relied on hand-crafted features over syntax trees that scale poorly to other languages and domains; in particular, to dialogue, which is one of the most interesting applications of sluice resolution. Syntactic information is arguably important for sluice resolution, but we show that multi-task learning with partial parsing as auxiliary tasks effectively closes the gap and buys us an additional 9% error reduction over previous work. Since we are not directly relying on features from partial parsers, our system is more robust to domain shifts, giving a 26% error reduction on embedded sluices in dialogue.

In this paper, we argue that same is fundamentally different from different, in that same imposes a discourse condition on eventualities, while different compares individuals. This difference has not been noted in previous literature. Furthermore, in the literature on same, there has been a persistent puzzle about the contribution of the definite article with which same must co-occur. We show that this puzzle is resolved once the contribution of same is adjusted to apply to eventualities: then the definite article can be interpreted in the usual way, as generating a presupposition about individuals.

Recent decades of research in linguistics have seen a shift towards empirical methods and an increased use of data from corpora as a basis for making claims about language (Sampson 2005). This trend has made its mark on research on the Nordic languages also, and the current special issue aims to show some of the breadth of research in this field. The issue is in its entirety devoted to contributions that use the methodology of corpus linguistics on Nordic language data. This includes research that investigates both historical and contemporary aspects of the languages of the Nordic region.

With increasingly high volumes of conversations across social media, the rapid detection of emotions is of significant strategic value to industry practitioners. Summarizing large volumes of text with computational linguistics and visual analytics allows for several new possibilities from general trend detection to specific applications in marketing practice, such as monitoring product launches, campaigns and public relations milestones. After collecting 1.6 million user-tagged feelings from 12 million online posts that mention emotions, we utilized machine learning techniques towards building an automatic 'feelings meter'; a tool for both researchers and practitioners to automatically detect emotional dimensions from text. Following several iterations, the test version has now taken shape as emotionVis, a dashboard prototype for inferring emotions from text while presenting the results for visual analysis.

Scientific reports 6, 34038 (2016). doi:10.1038/srep34038, Published by Nature Publishing Group, London

Y Since Webber (1978) it has been known that ellipsis interpretation sometimes requires inference. However, the literature on ellipsis has largely Ignored this: prominent accounts such as (Sag (1976); Williams (1977); Dalrymple et al. (1991); Fiengo and May (1994) and Hardt (1999)) provide no mechanism for inference in ellipsis resolution. The proposal in Rooth (1992) is an exception to this: Rooth gives several examples in which inference is required in ellipsis interpretation. He argues that ellipsis is subject to two different kinds of licensing conditions: an ellipsis-specific identity condition and a general focus-background condition. Rooth argues that the focus-background condition interacts with inference, while the ellipsis-specific identity condition does not. Rooth does not propose any constraints on inference. This presents an evident problem of overgeneration, since it is possible to come up with some chain of inferences to license virtually any interpretation of an elliptical expression.

This chapter presents a methodology and software tool for the analysis of Facebook data. In particular, it describes and demonstrates the analytical framework and computational aspects of the Social Data Analytics Tool (SODATO). SODATO fetches, stores, analyses and visualises data from Facebook walls. The method has been previously applied to the US elections of 2008 (Robertson 2011; Robertson et al. 2010a, 2010b). Here, we replicate and extend the analysis to Danish elections in 2011. Our substantive research question is to measure the extent to which Facebook walls function as online public spheres. To do so, we extract the Facebook walls of three prominent candidates in the 2011 Danish general election. Our findings show overlapping online public spheres and how different types of individuals inhabit these overlapping public spheres and how they provide structure and interpretive information for others.