VAN DIJK, TEUN A., News as Discourse Hillsdale, N.J.: Lawrence Erlbaum, 1988. $29.95 cloth. This book attempts the development of a "new, interdisciplinary theory of news in the press" (p. vii). It represents a very ambitious and somewhat speculative effort to weave together a broad range of existing news research approaches into a coherent, heuristic framework. Van Dijk succeeds in providing a useful summary of the literature in news research. Especially valuable is his discussion of recent European research. However, the overall framework is still at an early stage of development. Its utility remains to be demonstrated by future research. For some time now, communication researchers have talked of passing paradigms and ferment in the field. With the decline of past paradigms, our discipline has been left with a hodgepodge of small-scale theories. This is particularly true in the area of news research where various narrative, discourse and information processing theories abound. Van Dijk's book may signal a new era, an era in which efforts will be made to integrate existing conceptual fragments into broader frameworks. His work may be seen as providing a model for others who seek to make sense of our current proliferation of theories. Van Dijk's approach is centered in the tradition of discourse analysis, which evolved out of an integration of literary analysis and linguistics. However, he has aggressively modified earlier forms of discourse analysis in an effort to incorporate insights into the structure and interpretation of discourse derived from cognitive psychology. He is not content to simply apply discourse analysis to the evaluation of news stories. He recognizes the utility of constructing an approach which also considers the production of news by media practitioners and the interpretation of news by audience members. It is these broader concerns which set van Dijk's approach apart from previous analyses of news content. A central concept in van Dijk's theory is the notion of story schemas, which are defined as implicit structures that underlie typical stories. These schemas permit the easy production of news and also facilitate its interpretation by news consumers. The schema concept is at once powerful and ambiguous. There is growing research evidence that demonstrates the utility of positing the existence of cognitive structures (schemas) in people's minds which are activated by content cues and guide interpretion of all forms of communication. The schema concept helps to explain why complex and seemingly ambiguous messages often are easily interpreted by audience members. It also can explain why the same message can be interpreted in highly discrepent ways. If messages contain conflicting cues that lead people to activate different schemas, or if people don't share a homogeneous set of schemas, then it is likely that many contrasting interpretations of story content will be developed. But despite growing consensus concerning the utility of schema as a concept, researchers remain quite divided over both its definition and the type of research that will lead to the most useful findings. …

News as Discourse

User and task analysis for interface design

We discuss the impact of data bias on abusive language detection. We show that classification scores on popular datasets reported in previous work are much lower under realistic settings in which this bias is reduced. Such biases are most notably observed on datasets that are created by focused sampling instead of random sampling. Datasets with a higher proportion of implicit abuse are more affected than datasets with a lower proportion.

/pdf/detection-of-abusive-language-the-problem-of-biased-datasets-285ztbogcr.pdf

Detection of Abusive Language: the Problem of Biased Datasets

We present a system for the detection of the stance of headlines with regard to their corresponding article bodies The approach can be applied in fake news, especially clickbait detection scenarios The component is part of a larger platform for the curation of digital content; we consider veracity and relevancy an increasingly important part of curating online information We want to contribute to the debate on how to deal with fake news and related online phenomena with technological means, by providing means to separate related from unrelated headlines and further classifying the related headlines On a publicly available data set annotated for the stance of headlines with regard to their corresponding article bodies, we achieve a (weighted) accuracy score of 8959

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From Clickbait to Fake News Detection: An Approach based on Detecting the Stance of Headlines to Articles

Semantic similarity detection is a fundamental task in natural language understanding. Adding topic information has been useful for previous feature-engineered semantic similarity models as well as neural models for other tasks. There is currently no standard way of combining topics with pretrained contextual representations such as BERT. We propose a novel topic-informed BERT-based architecture for pairwise semantic similarity detection and show that our model improves performance over strong neural baselines across a variety of English language datasets. We find that the addition of topics to BERT helps particularly with resolving domain-specific cases.

tBERT: Topic Models and BERT Joining Forces for Semantic Similarity Detection

Previous work of ours on Semantic Storytelling uses text analytics procedures including Named Entity Recognition and Event Detection. In this paper, we outline our longer-term vision on Semantic Storytelling and describe the current conceptual and technical approach. In the project that drives our research we develop AI-based technologies that are verified by partners from industry. One long-term goal is the development of an approach for Semantic Storytelling that has broad coverage and that is, furthermore, robust. We provide first results on experiments that involve discourse parsing, applied to a concrete use case, "Explore the Neighbourhood!", which is based on a semi-automatically collected data set with documents about noteworthy people in one of Berlin's districts. Though automatically obtaining annotations for coherence relations from plain text is a non-trivial challenge, our preliminary results are promising. We envision our approach to be combined with additional features (NER, coreference resolution, knowledge graphs

Towards Discourse Parsing-inspired Semantic Storytelling.

Extraction and Normalization of Vague Time Expressions in German

Recommender systems assist legal professionals in finding relevant literature for supporting their case. Despite its importance for the profession, legal applications do not reflect the latest advances in recommender systems and representation learning research. Simultaneously, legal recommender systems are typically evaluated in small-scale user study without any public available benchmark datasets. Thus, these studies have limited reproducibility. To address the gap between research and practice, we explore a set of state-of-the-art document representation methods for the task of retrieving semantically related US case law. We evaluate text-based (e.g., fastText, Transformers), citation-based (e.g., DeepWalk, Poincare), and hybrid methods. We compare in total 27 methods using two silver standards with annotations for 2,964 documents. The silver standards are newly created from Open Case Book and Wikisource and can be reused under an open license facilitating reproducibility. Our experiments show that document representations from averaged fastText word vectors (trained on legal corpora) yield the best results, closely followed by Poincare citation embeddings. Combining fastText and Poincare in a hybrid manner further improves the overall result. Besides the overall performance, we analyze the methods depending on document length, citation count, and the coverage of their recommendations. We make our source code, models, and datasets publicly available at this https URL.

Julián Moreno-Schneider

Papers

Towards Discourse Parsing-inspired Semantic Storytelling.

Extraction and Normalization of Vague Time Expressions in German

Evaluating Document Representations for Content-based Legal Literature Recommendations