Luise Dürlich

Overview of ELOQUENT 2024 – Shared Tasks for Evaluating Generative Language Model Quality

Karlgren, Jussi; Dürlich, Luise; Gogoulou, Evangelia; Guillou, Liane et al.

Part of Experimental IR Meets Multilinguality, Multimodality, and Interaction (CLEF 2024), 2024

ELOQUENT CLEF Shared Tasks for Evaluation of Generative Language Model Quality

Karlgren, Jussi; Dürlich, Luise; Gogoulou, Evangelia; Guillou, Liane et al.

Part of Advances in Information Retrieval (ECIR 2024), 2024

Overview of the CLEF-2024 Eloquent Lab: Task 2 on HalluciGen

Dürlich, Luise; Gogoulou, Evangelia; Guillou, Liane; Nivre, Joakim et al.

Part of Working Notes of the Conference and Labs of the Evaluation Forum (CLEF 2024), p. 691-702, 2024

Optimizing Signal Management in a Vaccine Adverse Event Reporting System: A Proof-of-Concept with COVID-19 Vaccines Using Signs, Symptoms, and Natural Language Processing

Dong, Guojun; Bate, Andrew; Haguinet, François; Westman, Gabriel et al.

Part of Drug Safety, p. 173-182, 2024

• DOI for Optimizing Signal Management in a Vaccine Adverse Event Reporting System: A Proof-of-Concept with COVID-19 Vaccines Using Signs, Symptoms, and Natural Language Processing
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BERT based natural language processing for triage of adverse drug reaction reports shows close to human-level performance

Bergman, Erik; Dürlich, Luise; Arthurson, Veronica; Sundström, Anders et al.

Part of PLOS Digital Health, 2023

• DOI for BERT based natural language processing for triage of adverse drug reaction reports shows close to human-level performance
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Optimizing Signal Management in a Vaccine Adverse Event Reporting System: A Proof-of-Concept with COVID-19 Vaccines Using Signs, Symptoms, and Natural Language Processing

Dong, Guojun; Bate, Andrew; Haguinet, François; Westman, Gabriel et al.

Part of Drug Safety, p. 173-182, 2024

• DOI for Optimizing Signal Management in a Vaccine Adverse Event Reporting System: A Proof-of-Concept with COVID-19 Vaccines Using Signs, Symptoms, and Natural Language Processing
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BERT based natural language processing for triage of adverse drug reaction reports shows close to human-level performance

Bergman, Erik; Dürlich, Luise; Arthurson, Veronica; Sundström, Anders et al.

Part of PLOS Digital Health, 2023

• DOI for BERT based natural language processing for triage of adverse drug reaction reports shows close to human-level performance
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Nucleus Composition in Transition-Based Dependency Parsing

Nivre, Joakim; Basirat, Ali; Dürlich, Luise; Moss, Adam

Part of Computational Linguistics, p. 849-886, 2022

• DOI for Nucleus Composition in Transition-Based Dependency Parsing
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Overview of ELOQUENT 2024 – Shared Tasks for Evaluating Generative Language Model Quality

Karlgren, Jussi; Dürlich, Luise; Gogoulou, Evangelia; Guillou, Liane et al.

Part of Experimental IR Meets Multilinguality, Multimodality, and Interaction (CLEF 2024), 2024

ELOQUENT CLEF Shared Tasks for Evaluation of Generative Language Model Quality

Karlgren, Jussi; Dürlich, Luise; Gogoulou, Evangelia; Guillou, Liane et al.

Part of Advances in Information Retrieval (ECIR 2024), 2024

Overview of the CLEF-2024 Eloquent Lab: Task 2 on HalluciGen

Dürlich, Luise; Gogoulou, Evangelia; Guillou, Liane; Nivre, Joakim et al.

Part of Working Notes of the Conference and Labs of the Evaluation Forum (CLEF 2024), p. 691-702, 2024

On the Concept of Resource-Efficiency in NLP

Dürlich, Luise; Gogoulou, Evangelia; Nivre, Joakim

Part of Proceedings of the 24th Nordic Conference on Computational Linguistics (NoDaLiDa), p. 135-145, 2023

What Causes Unemployment?: Unsupervised Causality Mining from Swedish Governmental Reports

Dürlich, Luise; Nivre, Joakim; Stymne, Sara

Part of Proceedings of the Second Workshop on Resources and Representations for Under-Resourced Languages and Domains (RESOURCEFUL-2023), p. 25-29, 2023

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Cause and Effect in Governmental Reports: Two Data Sets for Causality Detection in Swedish

Dürlich, Luise; Reimann, Sebastian; Finnveden, Gustav; Nivre, Joakim et al.

Part of Proceedings of the First Workshop on Natural Language Processing for Political Sciences (PoliticalNLP), p. 46-55, 2022

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