Joseph Cornelius


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COVID-19 Twitter Monitor: Aggregating and Visualizing COVID-19 Related Trends in Social Media
Joseph Cornelius | Tilia Ellendorff | Lenz Furrer | Fabio Rinaldi
Proceedings of the Fifth Social Media Mining for Health Applications Workshop & Shared Task

Social media platforms offer extensive information about the development of the COVID-19 pandemic and the current state of public health. In recent years, the Natural Language Processing community has developed a variety of methods to extract health-related information from posts on social media platforms. In order for these techniques to be used by a broad public, they must be aggregated and presented in a user-friendly way. We have aggregated ten methods to analyze tweets related to the COVID-19 pandemic, and present interactive visualizations of the results on our online platform, the COVID-19 Twitter Monitor. In the current version of our platform, we offer distinct methods for the inspection of the dataset, at different levels: corpus-wide, single post, and spans within each post. Besides, we allow the combination of different methods to enable a more selective acquisition of knowledge. Through the visual and interactive combination of various methods, interconnections in the different outputs can be revealed.


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UZH@CRAFT-ST: a Sequence-labeling Approach to Concept Recognition
Lenz Furrer | Joseph Cornelius | Fabio Rinaldi
Proceedings of The 5th Workshop on BioNLP Open Shared Tasks

As our submission to the CRAFT shared task 2019, we present two neural approaches to concept recognition. We propose two different systems for joint named entity recognition (NER) and normalization (NEN), both of which model the task as a sequence labeling problem. Our first system is a BiLSTM network with two separate outputs for NER and NEN trained from scratch, whereas the second system is an instance of BioBERT fine-tuned on the concept-recognition task. We exploit two strategies for extending concept coverage, ontology pretraining and backoff with a dictionary lookup. Our results show that the backoff strategy effectively tackles the problem of unseen concepts, addressing a major limitation of the chosen design. In the cross-system comparison, BioBERT proves to be a strong basis for creating a concept-recognition system, although some entity types are predicted more accurately by the BiLSTM-based system.


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UZH@SMM4H: System Descriptions
Tilia Ellendorff | Joseph Cornelius | Heath Gordon | Nicola Colic | Fabio Rinaldi
Proceedings of the 2018 EMNLP Workshop SMM4H: The 3rd Social Media Mining for Health Applications Workshop & Shared Task

Our team at the University of Zürich participated in the first 3 of the 4 sub-tasks at the Social Media Mining for Health Applications (SMM4H) shared task. We experimented with different approaches for text classification, namely traditional feature-based classifiers (Logistic Regression and Support Vector Machines), shallow neural networks, RCNNs, and CNNs. This system description paper provides details regarding the different system architectures and the achieved results.