新万博体育下载_万博体育app【投注官网】

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Retrieval-powered Zero-shot Text Classification

Event Details
Date: 16.01.2023, 17:30 o'clock - 18:30 o'clock 
Location: N2045, Universit?tsstra?e 1, 86159 Augsburg
Organizer(s): Lehrstuhl für Biomedizinische Informatik, Data Mining und Data Analytics
Topics: Informatik, Gesundheit und 新万博体育下载_万博体育app【投注官网】izin
Series of events: 新万博体育下载_万博体育app【投注官网】ical Information Sciences
Event Type: Vortrag
Speaker(s): Prof. Dr. Carsten Eickhoff
? 新万博体育下载_万博体育app【投注官网】 of Augsburg

Im Wintersemester findet jeweils montags um 17:30h in H?rsaal N2045 die Vortragsreihe 新万博体育下载_万博体育app【投注官网】ical Information Sciences statt. Renommierte Wissenschaftlerinnen und Wissenschaftler unterschiedlicher Fachdisziplinen und Forschungsstandorte geben Einblicke in aktuelle Fragestellungen, Forschungsbereiche und Anwendungsgebiete dieses zunehmend bedeutsamen Forschungsfeldes.


Unstructured data, especially encoded in the form of natural language text, is one of the most prevalent and rapidly growing information types available to humankind. Unlocking the (often hidden) potential of such resources via natural language processing and understanding techniques can greatly support, or altogether enable, an exciting range of downstream applications. In this talk, I will give a brief high-level overview of ongoing NLP and IR efforts in my lab, before moving on to an investigation of zero-shot text classification in a diagnostic decision support setting. More than most other clinical inference tasks, primary care diagnosis experiences severely imbalanced long-tailed class distributions, under which some few classes are very well represented (e.g., congestive heart failure) while most others remain sparsely populated or even entirely unobserved in many clinical centers (e.g., rare diseases such as the Danbolt-Cross Syndrome). Such few- or zero-shot settings make a challenging stage on which to field conventional class-conditional machine learning models. In an attempt to address this issue, we draw from massive unsupervised domain resources such as Pub新万博体育下载_万博体育app【投注官网】 that are incorporated via an information retrieval step and observe significant performance improvements without the need for additional supervised training data.

Carsten Eickhoff is a Professor of E-Health and 新万博体育下载_万博体育app【投注官网】ical Data Science at the 新万博体育下载_万博体育app【投注官网】 of Tübingen where his lab specializes in the development of machine learning and natural language processing techniques with the goal of improving patient safety, individual health, and quality of medical care. Prior to joining Tübingen, he was the Manning Assistant Professor of 新万博体育下载_万博体育app【投注官网】ical and Computer Science at Brown 新万博体育下载_万博体育app【投注官网】. He received degrees from the 新万博体育下载_万博体育app【投注官网】 of Edinburgh and TU Delft, and was a postdoctoral fellow at ETH Zurich and Harvard 新万博体育下载_万博体育app【投注官网】. Carsten has authored more than 100 articles in computer science conferences (e.g., ICLR, ACL, SIGIR, WWW, KDD) and clinical journals (e.g., Nature Digital 新万博体育下载_万博体育app【投注官网】icine, The Lancet - Respiratory 新万博体育下载_万博体育app【投注官网】icine, Radiology, European Heart Journal). His research has been supported by the Swiss National Science Foundation, NSF, DARPA, IARPA, Google, Amazon, Microsoft and others. Aside from his academic endeavors, he is a founder and board member of several deep technology startups in the health sector that strive to translate technological innovation to improved safety and quality of life for patients.

More events of this series of events "新万博体育下载_万博体育app【投注官网】ical Information Sciences"

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