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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 伟德国际_伟德国际1946$娱乐app游戏izin
Series of events: 伟德国际_伟德国际1946$娱乐app游戏ical Information Sciences
Event Type: Vortrag
Speaker(s): Prof. Dr. Carsten Eickhoff
? 伟德国际_伟德国际1946$娱乐app游戏 of Augsburg

Im Wintersemester findet jeweils montags um 17:30h in H?rsaal N2045 die Vortragsreihe 伟德国际_伟德国际1946$娱乐app游戏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伟德国际_伟德国际1946$娱乐app游戏 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 伟德国际_伟德国际1946$娱乐app游戏ical Data Science at the 伟德国际_伟德国际1946$娱乐app游戏 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 伟德国际_伟德国际1946$娱乐app游戏ical and Computer Science at Brown 伟德国际_伟德国际1946$娱乐app游戏. He received degrees from the 伟德国际_伟德国际1946$娱乐app游戏 of Edinburgh and TU Delft, and was a postdoctoral fellow at ETH Zurich and Harvard 伟德国际_伟德国际1946$娱乐app游戏. 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 伟德国际_伟德国际1946$娱乐app游戏icine, The Lancet - Respiratory 伟德国际_伟德国际1946$娱乐app游戏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.

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