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  • 1.
    Dong, Guojun
    et al.
    University of Copenhagen, Denmark.
    Bate, Andrew
    GSK, United Kingdom; London School of Hygiene and Tropical Medicine, United Kingdom.
    Haguinet, François
    GSK, United Kingdom.
    Westman, Gabriel
    Uppsala University, Sweden.
    Dürlich, Luise
    RISE Research Institutes of Sweden, Digital Systems, Data Science. Uppsala University, Sweden.
    Hviid, Anders
    University of Copenhagen, Denmark; Statens Serum Institut, Denmark.
    Sessa, Maurizio
    University of Copenhagen, Denmark.
    Optimizing Signal Management in a Vaccine Adverse Event Reporting System: A Proof-of-Concept with COVID-19 Vaccines Using Signs, Symptoms, and Natural Language Processing2024In: Drug Safety, ISSN 0114-5916, E-ISSN 1179-1942, Vol. 47, no 2, p. 173-Article in journal (Refereed)
    Abstract [en]

    Introduction: The Vaccine Adverse Event Reporting System (VAERS) has already been challenged by an extreme increase in the number of individual case safety reports (ICSRs) after the market introduction of coronavirus disease 2019 (COVID-19) vaccines. Evidence from scientific literature suggests that when there is an extreme increase in the number of ICSRs recorded in spontaneous reporting databases (such as the VAERS), an accompanying increase in the number of disproportionality signals (sometimes referred to as ‘statistical alerts’) generated is expected. Objectives: The objective of this study was to develop a natural language processing (NLP)-based approach to optimize signal management by excluding disproportionality signals related to listed adverse events following immunization (AEFIs). COVID-19 vaccines were used as a proof-of-concept. Methods: The VAERS was used as a data source, and the Finding Associated Concepts with Text Analysis (FACTA+) was used to extract signs and symptoms of listed AEFIs from MEDLINE for COVID-19 vaccines. Disproportionality analyses were conducted according to guidelines and recommendations provided by the US Centers for Disease Control and Prevention. By using signs and symptoms of listed AEFIs, we computed the proportion of disproportionality signals dismissed for COVID-19 vaccines using this approach. Nine NLP techniques, including Generative Pre-Trained Transformer 3.5 (GPT-3.5), were used to automatically retrieve Medical Dictionary for Regulatory Activities Preferred Terms (MedDRA PTs) from signs and symptoms extracted from FACTA+. Results: Overall, 17% of disproportionality signals for COVID-19 vaccines were dismissed as they reported signs and symptoms of listed AEFIs. Eight of nine NLP techniques used to automatically retrieve MedDRA PTs from signs and symptoms extracted from FACTA+ showed suboptimal performance. GPT-3.5 achieved an accuracy of 78% in correctly assigning MedDRA PTs. Conclusion: Our approach reduced the need for manual exclusion of disproportionality signals related to listed AEFIs and may lead to better optimization of time and resources in signal management. © 2023, The Author(s).

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  • 2.
    Dürlich, Luise
    et al.
    RISE Research Institutes of Sweden, Digital Systems, Data Science. Uppsala University, Sweden.
    Gogoulou, Evangelia
    RISE Research Institutes of Sweden, Digital Systems, Data Science. KTH Royal Institute of Technology, Sweden.
    Nivre, Joakim
    RISE Research Institutes of Sweden, Digital Systems, Data Science. Uppsala University, Sweden.
    On the Concept of Resource-Efficiency in NLP2023In: Proceedings of the 24th Nordic Conference on Computational Linguistics (NoDaLiDa), 2023, p. 135-145Conference paper (Refereed)
    Abstract [en]

    Resource-efficiency is a growing concern in the NLP community. But what are the resources we care about and why? How do we measure efficiency in a way that is reliable and relevant? And how do we balance efficiency and other important concerns? Based on a review of the emerging literature on the subject, we discuss different ways of conceptualizing efficiency in terms of product and cost, using a simple case study on fine-tuning and knowledge distillation for illustration. We propose a novel metric of amortized efficiency that is better suited for life-cycle analysis than existing metrics.

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    fulltext
  • 3.
    Dürlich, Luise
    et al.
    RISE Research Institutes of Sweden, Digital Systems, Data Science. Uppsala University, Sweden.
    Nivre, Joakim
    RISE Research Institutes of Sweden, Digital Systems, Data Science. Uppsala University, Sweden.
    Stymne, Sara
    Uppsala University, Sweden.
    What Causes Unemployment?: Unsupervised Causality Mining from Swedish Governmental Reports2023In: RESOURCEFUL 2023 - Workshop on Resources and Representations for Under-Resourced Languages and Domains, Proceedings of the 2nd, Association for Computational Linguistics , 2023, p. 25-29Conference paper (Refereed)
    Abstract [en]

    Extracting statements about causality from text documents is a challenging task in the absence of annotated training data. We create a search system for causal statements about user-specified concepts by combining pattern matching of causal connectives with semantic similarity ranking, using a language model fine-tuned for semantic textual similarity. Preliminary experiments on a small test set from Swedish governmental reports show promising results in comparison to two simple baselines. 

  • 4.
    Dürlich, Luise
    et al.
    RISE Research Institutes of Sweden, Digital Systems, Data Science. Uppsala University, Sweden.
    Riemann, Sebastian
    Uppsala University, Sweden; Ruhr-Universitat Bochum, Germany .
    Finnveden, Gustav
    Uppsala University, Sweden.
    Nirve, Joakim
    RISE Research Institutes of Sweden, Digital Systems, Data Science. Uppsala University, Sweden.
    Stymne, Sara
    Uppsala University, Sweden.
    Cause and Effect in Governmental Reports: Two Data Sets for Causality Detection in Swedish2022In: Proceedings of the First Workshop on Natural Language Processing for Political Sciences (PoliticalNLP), Marseille, Framnce,. 24 June 2022, 2022, p. 46-55Conference paper (Refereed)
    Abstract [en]

    Causality detection is the task of extracting information about causal relations from text. It is an important task for different types of document analysis, including political impact assessment. We present two new data sets for causality detection in Swedish. The first data set is annotated with binary relevance judgments, indicating whether a sentence contains causality information or not. In the second data set, sentence pairs are ranked for relevance with respect to a causality query, containing a specific hypothesized cause and/or effect. Both data sets are carefully curated and mainly intended for use as test data. We describe the data sets and their annotation, including detailed annotation guidelines. In addition, we present pilot experiments on cross-lingual zero-shot and few-shot causality detection, using training data from English and German.

  • 5.
    Karlgren, Jussi
    et al.
    Silo AI, Finland.
    Dürlich, Luise
    RISE Research Institutes of Sweden, Digital Systems, Data Science.
    Gogoulou, Evangelia
    RISE Research Institutes of Sweden, Digital Systems, Data Science.
    Guillou, Liane
    RISE Research Institutes of Sweden, Digital Systems, Data Science.
    Nivre, Joakim
    RISE Research Institutes of Sweden, Digital Systems, Data Science.
    Sahlgren, Magnus
    Silo AI, Finland; AI Sweden, Sweden.
    Talman, Aarne
    Silo AI, Finland.
    ELOQUENT CLEF Shared Tasks for Evaluation of Generative Language Model Quality2024In: Lecture Notes in Computer Science, ISSN 0302-9743, E-ISSN 1611-3349, Vol. 14612 LNCS, p. 459-465Article in journal (Refereed)
    Abstract [en]

    ELOQUENT is a set of shared tasks for evaluating the quality and usefulness of generative language models. ELOQUENT aims to bring together some high-level quality criteria, grounded in experiences from deploying models in real-life tasks, and to formulate tests for those criteria, preferably implemented to require minimal human assessment effort and in a multilingual setting. The selected tasks for this first year of ELOQUENT are (1) probing a language model for topical competence; (2) assessing the ability of models to generate and detect hallucinations; (3) assessing the robustness of a model output given variation in the input prompts; and (4) establishing the possibility to distinguish human-generated text from machine-generated text.

  • 6.
    Nivre, Joakim
    et al.
    RISE Research Institutes of Sweden, Digital Systems, Data Science. Uppsala University, Sweden.
    Basirat, Ali
    Linköping University, Sweden.
    Dürlich, Luise
    RISE Research Institutes of Sweden, Digital Systems, Data Science. Uppsala University, Sweden.
    Moss, Adam
    Uppsala University, Sweden.
    Nucleus Composition in Transition-based Dependency Parsing2022In: Computational linguistics - Association for Computational Linguistics (Print), ISSN 0891-2017, E-ISSN 1530-9312, Vol. 48, no 4, p. 849-886Article in journal (Refereed)
    Abstract [en]

    Dependency-based approaches to syntactic analysis assume that syntactic structure can be analyzed in terms of binary asymmetric dependency relations holding between elementary syntactic units. Computational models for dependency parsing almost universally assume that an elementary syntactic unit is a word, while the influential theory of Lucien Tesnière instead posits a more abstract notion of nucleus, which may be realized as one or more words. In this article, we investigate the effect of enriching computational parsing models with a concept of nucleus inspired by Tesnière. We begin by reviewing how the concept of nucleus can be defined in the framework of Universal Dependencies, which has become the de facto standard for training and evaluating supervised dependency parsers, and explaining how composition functions can be used to make neural transition-based dependency parsers aware of the nuclei thus defined. We then perform an extensive experimental study, using data from 20 languages to assess the impact of nucleus composition across languages with different typological characteristics, and utilizing a variety of analytical tools including ablation, linear mixed-effects models, diagnostic classifiers, and dimensionality reduction. The analysis reveals that nucleus composition gives small but consistent improvements in parsing accuracy for most languages, and that the improvement mainly concerns the analysis of main predicates, nominal dependents, clausal dependents, and coordination structures. Significant factors explaining the rate of improvement across languages include entropy in coordination structures and frequency of certain function words, in particular determiners. Analysis using dimensionality reduction and diagnostic classifiers suggests that nucleus composition increases the similarity of vectors representing nuclei of the same syntactic type. 

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