CL4Health @ NAACL 2025

Patient-oriented language processing

• Important Dates
• Invited Talk
• Program
• Submissions
• Scope
• Shared Task
• Meeting
• Program Committee
• Organizers

Important Dates

CL4Health 2025: Program

Time Zone in Albuquerque, New Mexico, USA in May is MDT — Mountain Daylight Time

The submission site is:      https://softconf.com/naacl2025/cl4health2025

Dual submission policy: papers may NOT be submitted to the workshop if they are or will be concurrently submitted to another meeting or publication.

Scope

CL4Health fills the gap among the different biomedical language processing workshops by providing a general venue for a broad spectrum of patient-oriented language processing research. The second workshop on patient-oriented language processing follows the successful inaugural CL4Health workshop (collocated with LREC-COLING 2024), which clearly demonstrated the need for a computational linguistics venue that focuses on language related to health of the public.

CL4Health is concerned with the resources, computational approaches, and behavioral and socio-economic aspects of the public interactions with digital resources in search of health-related information that satisfies their information needs and guides their actions. The workshop invites papers concerning all areas of language processing focused on patients' health and health-related issues concerning the public. The issues include, but are not limited to accessibility and trustworthiness of health information provided to the public; explainable and evidence-supported answers to consumer-health questions; accurate summarization of patients' health records at their health-literacy level; understanding patients' non-informational needs through their language, and accurate and accessible interpretations of biomedical research. The topics of interest for the workshop include but are not limited to the following:

• Health-related information needs and online behaviors of the public;
• Quality assurance and ethics considerations in language technologies and approaches applied to text and other modalities for public consumption;
• Summarization of data from electronic health records for patients;
• Detection of misinformation in consumer health-related resources and mitigation of potential harms;
• Consumer health question answering (Community Question Answering)(CQA);
• Biomedical text simplification/adaptation;
• Dialogue systems to support patients' interactions with clinicians, healthcare systems, and online resources;
• Linguistic resources, data and tools for language technologies focusing on consumer health;
• Infrastructures and pre-trained language models for consumer health;

Shared Task

Perspective-aware Healthcare Answer Summarization (PerAnsSumm) will be co-located with the workshop. See details at https://peranssumm.github.io/.
In community/consumer health question answering, several aspects, such as question understanding and answer generation, have been studied for over a decade. A new and important question posed by this task is the different perspectives provided in the answers to questions posted to online forums. The responses to the questions offer different answer perspectives, e.g., personal experiences, factual information, and suggestions. Traditionally, the CQA answer summarization task has focused on a single best-voted answer as a reference summary. A single answer does not capture all the perspectives. Providing the answers in structured, perspective-specific summaries could better serve the information needs of end users. To address these gaps, this challenge introduces a novel perspective-specific answer summarization task within a CQA setup. The task will use the Perspective-aware healthcare Answer SuMmarizAtion (PUMA) dataset, a corpus of medical question-answer pairs created by the task organizers. The PUMA dataset consists of 3,167 CQA threads with approximately 10K answers filtered from the Yahoo! L6 corpus. Each answer in PUMA is annotated with five perspective spans: ‘cause’, ‘suggestion’, ‘experience’, ‘question’, and ‘information’.

Meeting

The workshop will be hybrid. Virtual attendees must be registered for the workshop to access the online environment.

Accepted papers will be presented as posters or oral presentations based on the reviewers’ recommendations.

Program Committee

• Mathilde Aguiar, Université Paris-Saclay, CNRS, France
• Sophia Ananiadou, National Centre for Text Mining and University of Manchester, UK
• Zainab Awan, UTS, Australia
• Leonor Barreiros, University of Lisbon, Portugal
• Abby Blocker, University of Cape Town, South Africa
• Leonardo Campillos-Llanos, Spanish National Research Council, Spain
• Amin Dada, Institute for AI in Medicine (IKIM), University Hospital Essen, Germany
• Luise Dürlich, Uppsala University, Sweden
• Manas Gaur, University of Maryland, Baltimore County, USA
• Sagar Goyal, DeepScribe Inc., USA
• Natalia Grabar, Université de Lille, France
• Tudor Groza, SingHealth Duke-NUS Institute of Precision Medicine, Singapore
• Deepak Gupta, National Library of Medicine, USA
• Ramakanth Kavuluru, University of Kentucky, USA
• Oren Kobo, Intel, Israel
• Vojtech Lanz, Charles University, Czech Republic
• Chuyuan Li, The University of British Columbia, Canada
• Zhiwei Liu, University of Manchester, UK
• Simon Meoni, Arkhn, France
• Shufan Ming, University of Illinois Urbana-Champaign, USA
• Jose G. Moreno, Paul Sabatier University - IRIT, France
• Aurélie Névéol, Université de Paris-Saclay, CNRS, LISN, Orsay, France
• Brian Ondov, Yale University, USA
• Tabea Pakull, Institute for Transfusion Medicine, University Hospital Essen, Germany
• Mehdi Parviz, University of Copenhagen, Denmark
• Anthony Rios, University of Texas at San Antonio, USA
• Miguel Rocha, University of Minho, Portugal
• Roland Roller, German Research Center for Artificial Intelligence, DFKI, Germany
• Abeed Sarker, Emory School of Medicine, USA
• Sarvesh Soni, National Library of Medicine, USA
• Paul Thompson, National Centre for Text Mining and University of Manchester, UK
• Grigorios Tsoumakas, Aristotle University of Thessaloniki, Greece
• Aswathy Velutharambath, University of Stuttgart, Germany
• Jinghua Xu, Heidelberg University, Germany
• Zhicheng Yang, PAII Inc., USA
• Dong Yuan, Google, USA
• Tianlin Zhang, The University of Manchester, UK
• Pierre Zweigenbaum, Université de Paris-Saclay, CNRS, LISN, Orsay, France

Organizers

• Dina Demner-Fushman, US National Library of Medicine
• Sophia Ananiadou, National Centre for Text Mining and University of Manchester, UK
• Paul Thompson, National Centre for Text Mining and University of Manchester, UK
• Deepak Gupta, US National Library of Medicine