Bio:   Manfred Stede is a Professor of applied computational linguistics at Potsdam University, Germany. His research and teaching activities revolve around issues in discourse structure and automatic discourse parsing, inlcuding applications in sentiment analysis and argument mining. For several years now, he actively collaborates with social scientists from different disciplines (political science, education science, communication science) on research questions involving political argumentation, social media analysis, and a focus on discourses about climate change. Stede is a (co-) author of four books, 30 journal papers, and 150 conference or workshop papers and book chapters.

Abstract:   The debate around climate change (CC) — its extent, its causes, and the necessary responses — is intense and of global importance. The ongoing discourses are a prominent object of study in several Social Sciences, while in the natural language processing community, this domain has so far received relatively little attention. In my talk, I first give a brief overview of types of approaches and data, and then report on two case studies that we are currently conducting in my research group. The first tackles the notion of "framing" (the perspective taken in viewing an issue) in CC-related editorials of the journals 'Nature' and 'Science': We proceed from a coarse-grained text-level labeling to increasingly detailed clause-level annotation of framing CC, and run experiments on automatic classification. The second involves a corpus of parliamentary speeches, press releases and tweets from the members of the German parliament (2017-2021) and compares their ways of addressing CC, contrasting on the one hand the different communication channels and on the other hand the party affiliations of the speakers.

Bio:   Sophia Ananiadou is Professor in the Department of Computer Science, University of Manchester, UK; Director, National Centre for Text Mining; Deputy Director, Institute of Data Science and Artificial Intelligence; Distinguished Research Fellow, Artificial Intelligence Research Centre, AIST, Japan and senior researcher at the Archimedes Research Centre, Greece. She was an Alan Turing Institute Fellow (2018-2023). Currently she is a member of the European Laboratory for Learning and Intelligent Systems (ELLIS). Her research contributions in Natural Language Processing include tasks such as information extraction, summarisation, simplification, emotion detection and misinformation analysis. Her research is deeply interdisciplinary. She has been active bridging the gap between Biomedicine and NLP, via the provision of tools for a variety of translational applications related with personalized medicine, drug discovery, database curation, risk assessment, disease prediction. Her research on recognising affective information (emotions, sentiments) has been applied to mental health applications and misinformation detection. Currently, she is also focusing on the development of LLMs for FinTech applications.

Abstract:   Social media serves as a key resource for analysing mental health through natural language processing (NLP) techniques like emotion detection. While current efforts focus on specific aspects of affective classification, such as sentiment polarity, they overlook regression tasks like sentiment intensity. We recognize the importance of emotional cues in mental health detection and propose MentaLLaMA, an interpretable LLM series for social media mental health analysis. Emotions and sentiment also play vital roles in detecting misinformation and conspiracy theories. However, existing LLM-based approaches often neglect the emotional dimensions of misinformation. By integrating affective cues into automated detection systems, we can improve accuracy. We'll showcase an open-source LLM that leverages emotional cues for enhanced detection of conspiracy theories, utilizing a novel conspiracy dataset.

Bio:   Kevin D. Ashley, Ph.D., is an expert on computer modeling of legal reasoning. He was selected as a Fellow of the American Association of Artificial Intelligence “for significant contributions in computationally modeling case-based and analogical reasoning in law and practical ethics.” He has been a principal investigator of a number of National Science Foundation grants and is co-editor in chief of Artificial Intelligence and Law, the journal of record in the field. He wrote Modeling Legal Argument: Reasoning with Cases and Hypotheticals (MIT Press/Bradford Books, 1990) and Artificial Intelligence and Legal Analytics: New Tools for Law Practice in the Digital Age (Cambridge University Press, 2017). He is a full professor at the School of Law, a senior scientist at the Learning Research and Development Center, and a member of the Intelligent Systems Program of the University of Pittsburgh.

Abstract:   Researchers in AI and Law have applied text analytic tools, including Natural Language Processing and Machine Learning, to predict the outcomes of legal cases and to attempt to explain the predictions in terms legal professionals would understand. Such explanations require legal knowledge, but integrating legal knowledge with deep learning can be problematic. Formerly, in modeling how legal professionals argue with cases and analogies, researchers explicitly represented aspects of the legal knowledge that advocates and judges employ in predicting, explaining, and arguing for and against case outcomes, such as legal issues, rules, factors, and values. Representing cases in terms of the applicable knowledge, however, was a manual process. More recently, researchers are employing text analytics to bridge the gap automatically between case texts and their argument models. Advances in large language models and generative AI have expanded the approaches for automatically representing the knowledge but raise new questions about the roles, if any, that argument models and the associated legal knowledge will play in an age of generative AI. This talk surveys a series of recent projects that bear on these questions.

Bio:   Dr. Swarnendu Ghosh is an academician and researcher specializing in compact and innovative deep learning solutions for computer vision, generative AI, and NLP. With a PhD in Computer Science and Engineering from Jadavpur University, India, and extensive research experience as an Erasmus Mundus Fellow at the University of Evora, Portugal, Dr. Ghosh has honed his expertise in developing cutting-edge methodologies for object recognition, image segmentation, and knowledge graph generation across diverse domains. His academic journey includes a Master's degree from Jadavpur University, where he explored sentiment classification using discourse analysis and graph kernels. Swarnendu has also contributed significantly to multiple Government research projects, such as developing knowledge graphs from images and crafting event-guided natural scene description frameworks for real-time applications. His rapidly growing research profile has already gathered over 800 citations in a short duration and boasts eminent journals such as ACM Computing Surveys, Pattern Recognition and Computer Science Reviews. Dr. Ghosh's expertise extends to teaching and mentoring roles, currently serving as an Associate Professor at IEM Kolkata. He is the founder of the IEM Centre of Excellence for Data Science and he is also the coordinator of the Innovation and Entrepreneurship Development Cell.

Abstract:   Digital twins have recently gathered significant interest in the healthcare community. This concept promises to unlock various previously unavailable services such as remote monitoring, advanced visualization, simulation of medical procedures, predictive analytics, demographic studies, and so on. At present research in this area is localized and conducted independently. Thus, effective deployment of digital twins in healthcare is still a work in progress due to inconsistent data representation and isolated innovation without effective integration at large scale. Knowledge representation plays a vital role in structuring, integrating, and reasoning over heterogeneous healthcare data sources such as electronic health records, genomics data, clinical guidelines, reports, medical literature, and more. The process of digitization is relevant not only to patients but also to healthcare professionals, infrastructure facilities, devices, insurance providers, and even historical records. This work proposes to thoroughly highlight this research gap and the current initiatives addressing these issues. It aims to review and consolidate existing efforts in standardizing data structures for healthcare digital twins, with a focus on interoperability, representation and integration across diverse healthcare domains.