This dissertation explores the application of zero-shot text classification, a technique for categorizing texts without annotated data in the target domain. A true zero-shot setting breaks with the conventions of the traditional supervised machine learning paradigm that relies on quantitative in-domain evaluation for optimization, performance measurement, and model selection. The dissertation summarizes existing research to build a theoretical foundation for zero-shot methods, emphasizing efficiency and transparency. It benchmarks selected approaches across various tasks and datasets to understand their general performance, strengths, and weaknesses, mirroring the model selection process. On this foundation, two case studies demonstrate the application of zero-shot text classification: The first engages with historical German stock market reports, utilizing zero-shot methods for aspect-based sentiment classification. The case study reveals that although there are qualitative differences between finetuned and zero-shot approaches, the aggregated results are not easily distinguishable, sparking a discussion about the practical implications. The second case study integrates zero-shot text classification into a civil engineering document management system, showcasing how the flexibility of zero-shot models and the omission of the training process can benefit the development of prototype software, at the cost of an unknown performance. These findings indicate that, although zero-shot text classification works for the exemplary cases, the results are not generalizable. Taking up the findings of these case studies, the dissertation discusses dilemmas and theoretical considerations that arise from omitting the in-domain evaluation of applying zero-shot text classification. It concludes by advocating a broader focus beyond traditional quantitative metrics in order to build trust in zero-shot text classification, highlighting their practical utility as well as the necessity for further exploration as these technologies evolve.:1 Introduction 1.1 Problem Context 1.2 Related Work 1.3 Research Questions & Contribution 1.4 Author’s Publications 1.5 Structure of This Work 2 Research Context 2.1 The Current State of Text Classification 2.2 Efficiency 2.3 Approaches to Addressing Data Scarcity in Machine Learning 2.4 Challenges of Recent Developments 2.5 Model Sizes and Hardware Resources 2.6 Conclusion 3 Zero-shot Text Classification 3.1 Text Classification 3.2 State-of-the-Art in Text Classification 3.3 Neural Network Approaches to Data-Efficient Text Classification 3.4 Zero-shot Text Classification 3.5 Application 3.6 Requirements for Zero-shot Models 3.7 Approaches to Transfer Zero-shot 3.7.1 Terminology 3.7.2 Similarity-based and Siamese Networks 3.7.3 Language Model Token Predictions 3.7.4 Sentence Pair Classification 3.7.5 Instruction-following Models or Dialog-based Systems 3.8 Class Name Encoding in Text Classification 3.9 Approach Selection 3.10 Conclusion 4 Model Performance Survey 4.1 Experiments 4.1.1 Datasets 4.1.2 Model Selection 4.1.3 Hypothesis Templates 4.2 Zero-shot Model Evaluation 4.3 Dataset Complexity 4.4 Conclusion 5 Case Study: Historic German Stock Market Reports 5.1 Project 5.2 Motivation 5.3 Related Work 5.4 The Corpus and Dataset - Berliner Börsenzeitung 5.4.1 Corpus 5.4.2 Sentiment Aspects 5.4.3 Annotations 5.5 Methodology 5.5.1 Evaluation Approach 5.5.2 Trained Pipeline 5.5.3 Zero-shot Pipeline 5.5.4 Dictionary Pipeline 5.5.5 Tradeoffs 5.5.6 Label Space Definitions 5.6 Evaluation - Comparison of the Pipelines on BBZ 5.6.1 Sentence-based Sentiment 5.6.2 Aspect-based Sentiment 5.6.3 Qualitative Evaluation 5.7 Discussion and Conclusion 6 Case Study: Document Management in Civil Engineering 6.1 Project 6.2 Motivation 6.3 Related Work 6.4 The Corpus and Knowledge Graph 6.4.1 Data 6.4.2 BauGraph – The Knowledge Graph 6.5 Methodology 6.5.1 Document Insertion Pipeline 6.5.2 Frontend Integration 6.6 Discussion and Conclusion 7 MLMC 7.1 How it works 7.2 Motivation 7.3 Extensions of the Framework 7.4 Other Projects 7.4.1 Product Classification 7.4.2 Democracy Monitor 7.4.3 Climate Change Adaptation Finance 7.5 Conclusion 8 Discussion: The Five Dilemmas of Zero-shot 8.1 On Evaluation 8.2 The Five Dilemmas of Zero-shot 8.2.1 Dilemma of Evaluation or Are You Working at All? 8.2.2 Dilemma of Comparison or How Do I Get the Best Model? 8.2.3 Dilemma of Annotation and Label Definition or Are We Talking about the Same Thing? 8.2.4 Dilemma of Interpretation or Am I Biased? 8.2.5 Dilemma of Unsupervised Text Classification or Do I Have to Trust You? 8.3 Trust in Zero-shot Capabilities 8.4 Conclusion 9 Conclusion 9.1 Summary . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 139 9.1.1 RQ1: Strengths and Weaknesses . . . . . . . . . . . . . . . . 139 9.1.2 RQ2: Application Studies . . . . . . . . . . . . . . . . . . . . 141 9.1.3 RQ3: Implications . . . . . . . . . . . . . . . . . . . . . . . . 143 9.2 Final Thoughts & Future Directions . . . . . . . . . . . . . . . . . . 144 References 147 A Appendix for Survey Chapter A.1 Model Selection . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 179 A.2 Task-specific Hypothesis Templates . . . . . . . . . . . . . . . . . . 180 A.3 Fractions of SotA . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 179 181 B Uncertainty vs. Accuracy 182 C Declaration of Authorship 185 D Declaration: Use of AI-Tools 186 E Bibliographic Data 187 / In dieser Dissertation wird die Anwendung von Zero-Shot-Textklassifikation -- die Kategorisierung von Texten ohne annotierte Daten in der Anwendungsdomäne -- untersucht. Ein echter Zero-Shot-Ansatz bricht mit den Konventionen des traditionellen überwachten maschinellen Lernens, welches auf einer quantitativen Evaluierung in der Zieldomäne zur Optimierung, Performanzmessung und Modellauswahl (model selection) basiert. Eine Zusammenfassung bestehender Forschungsarbeiten bildet die theoretische Grundlage für die verwendeten Zero-Shot-Methoden, wobei Effizienz und Transparenz im Vordergrund stehen. Ein Vergleich ausgewählter Ansätze mit verschiedenen Tasks und Datensätzen soll allgemeine Stärken und Schwächen aufzeigen und den Prozess der Modellauswahl widerspiegeln. Auf dieser Grundlage wird die Anwendung der Zero-Shot-Textklassifikation anhand von zwei Fallstudien demonstriert: Die erste befasst sich mit historischen deutschen Börsenberichten, wobei Zero-Shot zur aspekt-basierten Sentiment-Klassifikation eingesetzt wird. Es zeigt sich, dass es zwar qualitative Unterschiede zwischen trainierten und Zero-Shot-Ansätzen gibt, dass die aggregierten Ergebnisse aber nicht leicht zu unterscheiden sind, was Überlegungen zu praktischen Implikationen anstößt. Die zweite Fallstudie integriert Zero-Shot-Textklassifikation in ein Dokumentenmanagementsystem für das Bauwesen und zeigt, wie die Flexibilität von Zero-Shot-Modellen und der Wegfall des Trainingsprozesses die Entwicklung von Prototypen vereinfachen können -- mit dem Nachteil, dass die Genauigkeit des Modells unbekannt bleibt. Die Ergebnisse zeigen, dass die Zero-Shot-Textklassifikation in den Beispielanwendungen zwar annähernd funktioniert, die Ergebnisse aber nicht leicht verallgemeinerbar sind. Im Anschluss werden Dilemmata und theoretische Überlegungen erörtert, die sich aus dem Wegfall der Evaluierung in der Zieldomäne von Zero-Shot-Textklassifikation ergeben. Abschließend wird ein breiterer Fokus über die traditionellen quantitativen Metriken hinaus vorgeschlagen, um Vertrauen in die Zero-Shot-Textklassifikation aufzubauen und den praktischen Nutzen zu verbessern. Die Überlegungen zeigen aber auch die Notwendigkeit weiterer Forschung im Zuge der Weiterentwicklung dieser Technologien.:1 Introduction 1.1 Problem Context 1.2 Related Work 1.3 Research Questions & Contribution 1.4 Author’s Publications 1.5 Structure of This Work 2 Research Context 2.1 The Current State of Text Classification 2.2 Efficiency 2.3 Approaches to Addressing Data Scarcity in Machine Learning 2.4 Challenges of Recent Developments 2.5 Model Sizes and Hardware Resources 2.6 Conclusion 3 Zero-shot Text Classification 3.1 Text Classification 3.2 State-of-the-Art in Text Classification 3.3 Neural Network Approaches to Data-Efficient Text Classification 3.4 Zero-shot Text Classification 3.5 Application 3.6 Requirements for Zero-shot Models 3.7 Approaches to Transfer Zero-shot 3.7.1 Terminology 3.7.2 Similarity-based and Siamese Networks 3.7.3 Language Model Token Predictions 3.7.4 Sentence Pair Classification 3.7.5 Instruction-following Models or Dialog-based Systems 3.8 Class Name Encoding in Text Classification 3.9 Approach Selection 3.10 Conclusion 4 Model Performance Survey 4.1 Experiments 4.1.1 Datasets 4.1.2 Model Selection 4.1.3 Hypothesis Templates 4.2 Zero-shot Model Evaluation 4.3 Dataset Complexity 4.4 Conclusion 5 Case Study: Historic German Stock Market Reports 5.1 Project 5.2 Motivation 5.3 Related Work 5.4 The Corpus and Dataset - Berliner Börsenzeitung 5.4.1 Corpus 5.4.2 Sentiment Aspects 5.4.3 Annotations 5.5 Methodology 5.5.1 Evaluation Approach 5.5.2 Trained Pipeline 5.5.3 Zero-shot Pipeline 5.5.4 Dictionary Pipeline 5.5.5 Tradeoffs 5.5.6 Label Space Definitions 5.6 Evaluation - Comparison of the Pipelines on BBZ 5.6.1 Sentence-based Sentiment 5.6.2 Aspect-based Sentiment 5.6.3 Qualitative Evaluation 5.7 Discussion and Conclusion 6 Case Study: Document Management in Civil Engineering 6.1 Project 6.2 Motivation 6.3 Related Work 6.4 The Corpus and Knowledge Graph 6.4.1 Data 6.4.2 BauGraph – The Knowledge Graph 6.5 Methodology 6.5.1 Document Insertion Pipeline 6.5.2 Frontend Integration 6.6 Discussion and Conclusion 7 MLMC 7.1 How it works 7.2 Motivation 7.3 Extensions of the Framework 7.4 Other Projects 7.4.1 Product Classification 7.4.2 Democracy Monitor 7.4.3 Climate Change Adaptation Finance 7.5 Conclusion 8 Discussion: The Five Dilemmas of Zero-shot 8.1 On Evaluation 8.2 The Five Dilemmas of Zero-shot 8.2.1 Dilemma of Evaluation or Are You Working at All? 8.2.2 Dilemma of Comparison or How Do I Get the Best Model? 8.2.3 Dilemma of Annotation and Label Definition or Are We Talking about the Same Thing? 8.2.4 Dilemma of Interpretation or Am I Biased? 8.2.5 Dilemma of Unsupervised Text Classification or Do I Have to Trust You? 8.3 Trust in Zero-shot Capabilities 8.4 Conclusion 9 Conclusion 9.1 Summary . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 139 9.1.1 RQ1: Strengths and Weaknesses . . . . . . . . . . . . . . . . 139 9.1.2 RQ2: Application Studies . . . . . . . . . . . . . . . . . . . . 141 9.1.3 RQ3: Implications . . . . . . . . . . . . . . . . . . . . . . . . 143 9.2 Final Thoughts & Future Directions . . . . . . . . . . . . . . . . . . 144 References 147 A Appendix for Survey Chapter A.1 Model Selection . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 179 A.2 Task-specific Hypothesis Templates . . . . . . . . . . . . . . . . . . 180 A.3 Fractions of SotA . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 179 181 B Uncertainty vs. Accuracy 182 C Declaration of Authorship 185 D Declaration: Use of AI-Tools 186 E Bibliographic Data 187

The United States experiences more extreme wind events than any other country, owing to its extensive coastlines, central regions prone to tornadoes, and varied climate that together create a wide array of wind phenomena. Despite advanced meteorological forecasts, these events continue to have significant impacts on infrastructure due to the knowledge gap between hazard prediction and tangible impact. Consequently, disaster managers are increasingly interested in understanding the impacts of past wind events that can assist in formulating strategies to enhance community resilience. However, this data is often non-structured and embedded in various agency documents. This makes it challenging to access and use the data effectively. Therefore, it is important to investigate approaches that can distinguish and extract impact data from non-essential information. This research aims at exploring methods that can identify, extract, and summarize sentences containing impact data. The significance of this study lies in addressing the scarcity of historical impact data related to structural and community damage, given that such information is dispersed across multiple briefings and damage reports. The research has two main objectives. The first is to extract sentences providing information on infrastructure, or community damage. This task uses Zero-shot text classification with the large version of the Bidirectional and Auto-Regressive Transformers model (BART-large) pre-trained on the multi-nominal language inference (MNLI) dataset. The model identifies the impact sentences by evaluating entailment probabilities with user-defined impact keywords. This method addresses the absence of manually labeled data and establishes a framework applicable to various reports. The second objective transforms this extracted data into easily digestible summaries. This is achieved by using a pre-trained BART-large model on the Cable News Network (CNN) Daily Mail dataset to generate abstractive summaries, making it easier to understand the key points from the extracted impact data. This approach is versatile, given its dependence on user-defined keywords, and can adapt to different disasters, including tornadoes, hurricanes, earthquakes, floods, and more. A case study will demonstrate this methodology, specifically examining the Hurricane Ian impact data found in the Structural Extreme Events Reconnaissance (StEER) damage report. / Master of Science / The U.S. sees more severe windstorms than any other country. These storms can cause significant damage, despite the availability of warnings and alerts generated from weather forecast systems up to 72 hours before the storm hits. One challenge is the ineffective communication between emergency managers and at-risk communities, which can hinder timely evacuation and preparation. Additionally, data about past storm damages are often mixed up with non-actionable information in many different reports, making it difficult to use the data to enhance future warnings and readiness for upcoming storms. This study tries to solve this problem by finding ways to identify, extract, and summarize information about damage caused by windstorms. It is an important step toward using historical data to prepare for future events. Two main objectives guide this research. The first involves extracting sentences in these reports that provide information on damage to buildings, infrastructure, or communities. We're using a machine learning model to sort the sentences into two groups: those that contain useful information and those that do not. The second objective revolves around transforming this extracted data into easily digestible summaries. The same machine learning model is then trained in a different way, to create these summaries. As a result, critical data can be presented in a more user-friendly and effective format, enhancing its usefulness to disaster managers.

In our study, we conduct a comparative analysis between dictionary-based sentiment analysis and entailment zero-shot text classification for German sentiment analysis. We evaluate the performance of a selection of dictionaries on eleven data sets, including four domain-specific data sets with a focus on historic German language. Our results demonstrate that, in the majority of cases, zero-shot text classification outperforms general-purpose dictionary-based approaches but falls short of the performance achieved by specifically fine-tuned models. Notably, the zero-shot approach exhibits superior performance, particularly in historic German cases, surpassing both general-purpose dictionaries and even a broadly trained sentiment model. These findings indicate that zero-shot text classification holds significant promise as an alternative, reducing the necessity for domain-specific sentiment dictionaries and narrowing the availability gap of off-the-shelf methods for German sentiment analysis. Additionally, we thoroughly discuss the inherent trade-offs associated with the application of these approaches.